Query auto completion (QAC) (Cai and De Rijke, 2016) is the standard component of search engines in industry. Given a prefix, the system returns a ranked list of completed queries that match users’ intents. Query auto completion enhances user experience in two ways: (1) it saves user keystrokes and returns search results in less time; (2) it guides users to type ”better” queries that are more likely to lead to desirable search results. For example, given a prefix ”soft”, ”software engineer” is a better query than ”software programmer”, since the former is a more commonly used job title.
A typical QAC system takes a generate-then-rank two-step framework. The candidate generation component returns the most frequent queries that match the prefix, by memorizing the mapping from prefixes to queries based on search logs (Bar-Yossef and Kraus, 2011). The candidate ranking component extracts features from candidates and uses them to produce the final ranking order. Both components do not involve intense computation so the whole process can be finished within several milliseconds, in order to meet online latency requirements.
However, this traditional approach does not fully exploit the context in the query prefix. For example, in the generation phase, for unseen prefixes, only the last word of prefix is used to generate candidates (Mitra and Craswell, 2015); in the ranking phase, the most effective feature is the query frequency collected from search log, which lacks deep semantics understanding.
To enable semantic text understanding, neural networks based methods are applied to QAC. Early works(Mitra and Craswell, 2015)
focus on the ranking stage: Convolutional Neural Networks (CNN)(Shen et al., 2014) are adopted to measure the semantic coherence between the query prefix (user input) and suggested suffixes in the ranking phase. Recently, an end-to-end approach for both generation and ranking (Park and Chiba, 2017)
is proposed: a neural language model is trained to measure the probability of a sequence of tokens. During decoding, candidate generation and ranking are performed for multiple iterations during beam search(Lowerre, 1976). While neural language models show better sequence modeling power over CNN, they could take up to 1 second (Wang et al., 2018), making productionization infeasible.
In this work, our goal is to build an QAC system with more effective query context utilization for real world search systems, while meeting the industry latency standard. We make improvements in the two-stage generate-then-rank framework: (1) In candidate generation, we extend a previous work (Mitra and Craswell, 2015) by incorporating more information from unseen prefixes to generate meaningful candidates. (2) In candidate ranking, we adopt neural language modeling, a more natural approach to model the coherence between a word and its previous sequence. To overcome the latency challenge, we optimize the latency by approximating computation of word probability with a much more efficient structure, reducing 95% latency (55ms to 3ms). Offline experiments on public datasets show significant improvement in terms of relevance and latency. We also train our model on the LinkedIn job search dataset and deploy it in production with CPU serving.
In summary, the contribution of this paper is
We developed an effective candidate generation method that maximizes recall of clicked queries through better context utilization.
We developed an optimized neural language model for candidate ranking, which has similar sequence modeling power as general neural language models, yet with significantly lower serving latency.
We deploy this efficient QAC system into commercial search engines, and observe significant product impact.
2. Related Work
In this session, we first introduce traditional methods for QAC, then discuss how neural networks are applied for QAC, followed by details of neural language models.
2.1. Traditional Approaches for QAC
Most of the previous works adopt a two-step framework – candidate generation and candidate ranking (Cai and De Rijke, 2016). The former aims to increase recall, and the latter focuses on increasing precision. Candidate generation components return a list of completed queries for a given prefix. Most works are based on prefix-to-query statistics calculated from search logs. For example, Most Popular Completion (MPC) (Bar-Yossef and Kraus, 2011) directly looks up the top N most frequent historical queries that start with the entire prefix. Mitra et al. (Mitra and Craswell, 2015) extend this method and generate candidates for rare or unseen prefixes by exploiting information from the last word in the prefix. Other works investigate the problem of generating candidates directly from documents when search logs are not available (Bhatia et al., 2011; Maxwell et al., 2017). In the candidate generation phase, we further extend (Mitra and Craswell, 2015) to use more context to generate better candidates.
After candidates are generated, candidate ranking components compute a relevance score for each candidate. Different sources of information have been exploited to improve the ranking, including context or session information (Bar-Yossef and Kraus, 2011; Jiang et al., 2014), time/popularity-sensitivity (Shokouhi and Radinsky, 2012; Whiting and Jose, 2014; Cai et al., 2014), personalization (Shokouhi, 2013; Cai et al., 2014), user behaviors (Li et al., 2014; Mitra et al., 2014; Hofmann et al., 2014; Zhang et al., 2015), and click through logs (Li et al., 2017). Our paper does not use any of these additional information and focuses on the effectiveness and efficiency of QAC given general query logs. Therefore, our work is orthogonal to methods using additional information and can benefit these methods as well.
2.2. Deep Learning Approach for QAC
In order to tackle insufficient text understanding in traditional approaches, deep learning approaches are adopted in recent years.
One line of work is applying neural networks to extract semantic embeddings from queries and perform ranking. In (Mitra and Craswell, 2015), Convolutional Latent Semantic Model (CLSM) (Shen et al., 2014) is used for QAC ranking. This model applies Convolutional Neural Networks (CNN) (LeCun and Bengio, 1995)
to extract an embedding for query prefix and suffix strings. Then the cosine similarity score between the prefix and suffix embedding determines the ranking.
. In such work, a Long Short Term Memory (LSTM)(Hochreiter and Schmidhuber, 1997) based neural language model (Mikolov et al., 2010) is trained on complete queries. After that beam search (Lowerre, 1976) is used for decoding. In beam search, there are many iterations of generation and ranking, which yield impressive relevance performance with a large computation overhead. There are several advantages of neural language modeling. One advantage of the neural language modeling architecture is that additional features can be seamlessly incorporated. For example, personalization can be modeled by incorporating user ID embeddings (Jaech and Ostendorf, 2018; Fiorini and Lu, 2018; Jiang et al., 2018) in the network. Time aware (Fiorini and Lu, 2018) and spelling errors aware (Wang et al., 2018) models are also developed under this framework. Another advantage is that language modeling is more effective at sequence coherence modeling, supported by its probabilistic interpretation .
2.3. Neural Language Modeling
Neural language models measure the probability of a text sequence. Bengio et al. (Bengio et al., 2003) propose a neural language model, where the probability of the next word is computed based on the embeddings of previous several words. Mikolov et al. (Mikolov et al., 2010)
use Recurrent Neural Networks (RNN)(Williams and Zipser, 1989) to summarize a sequence of any length into a hidden state of RNN, which generalizes the context better. Following these works are word level (Sundermeyer et al., 2012) and character level (Kim et al., 2016) neural language models.
However, these neural language modeling approaches are time consuming in both training and decoding stages – in the computation of word probability, these methods compute a costly normalization term which requires iterating over all words in the vocabulary. In order to resolve this issue, unnormalized language models (Gutmann and Hyvärinen, 2010; Devlin et al., 2014) are proposed, targeting latency reduction. The idea is to approximate the normalization over the whole vocabulary in word probability computation. Unnormalized language models have been applied on machine translation (Devlin et al., 2014), speech recognition (Chen et al., 2015; Sethy et al., ) and word embedding pretraining (Mnih and Kavukcuoglu, 2013). We propose an efficient unnormalized language model approach for QAC ranking, and deploy it into LinkedIn’s search engines.
3. An Efficient Neural Query Auto Completion System
We propose an efficient neural query auto completion system that consists of two phases: candidate generation and candidate ranking. In candidate generation, we aim to increase recall of candidates with more context utilization. This step is finished within 1 millisecond. In candidate ranking, we design an efficient unnormalized neural language model that effectively models the query sequence. Therefore, the following content focuses on (1) how to exploit more contexts for both generation and ranking, as well as (2) how to minimize the computation cost in neural language model based ranking.
3.1. Candidate Generation
|cheapest flights from seattle to|
|cheapest flights from seattle to sfo|
|cheapest flights from seattle to vancouver|
|cheapest flights from seattle to airport|
|cheapest flights from seattle to study|
The candidate generation component returns a list of queries for a given prefix. The first step is to collect background data within a certain time range. The background data contain queries and their corresponding frequency computed from search logs within the time range. The second step is to build a prefix-to-query mapping based on the background data. Given a prefix, candidates are generated by looking up the mapping and choosing the top N most frequent queries. The whole process can be optimized in microseconds with the Apache Finite State Transducer (FST) library.111https://lucene.apache.org/core/7_3_1/core/org/apache/lucene/util/fst/FST.html
One obvious issue is that the prefix may never be seen in the background data. Mitra and Craswell (Mitra and Craswell, 2015) overcome this issue by exploiting suffixes. First, 100k most frequent suffixes are collected from the background queries. Then, for an unseen user input such as “cheapest flights from seattle to”, the algorithm extracts the last word “to”,222It could also be the last incomplete word. For example, if the prefix is ”cheapest flights from seattle t”, then the last incomplete word is ”t”. and uses it to match any suffixes that start with “to”. Therefore, they are able to find suffixes such as “to dc”, “to bermuda”, etc., which can be appended to “cheapest flights from seattle”. We refer to this method as LastWordGeneration (LWG).
Note that two separate FSTs are built: one is built on background queries, and the other one is built on all suffix n-grams of the queries. We call these two FSTsQueryFST and SuffixFST respectively.
It’s observed that the quality of retrieved suffixes is not high, when only the last word is used for matching. We believe using more contexts to match suffixes should yield more relevant results. Therefore, we extend this approach by greedily matching the longest last few words, instead of only the last one word. For the unseen prefix “cheapest flights from seattle to”, we first try to find suffixes that start with “flights from seattle to”, then “from seattle to”, “seattle to”, and finally ”to”. An example is shown in Table 1. By using more prefix tokens, it works effectively for recall improvement. This approach is referred to as Maximum Context Generation (MCG) and described in Algorithm 1.
3.2. Candidate Ranking
In this section, we focus on how to rank the generated candidates with neural networks. As shown in Figure 1, our system consists of two major components: an ”unnormalized” language modeling (Devlin et al., 2014; Sethy et al., ) layer and a pairwise learning-to-rank (Burges et al., 2005) layer. The unnormalized language model layer computes a score for a query candidate efficiently. Then learning-to-rank objective functions are applied on the scores of the clicked and non-clicked query pairs. These two components are trained together in an end-to-end fashion.
To score a query, an intuitive way is neural language modeling with LSTM (Park and Chiba, 2017) that computes the sequence probability as the query scores, as previously discussed in Section 2.2. The log probability of the query is computed as:
where is the hidden state of an LSTM (Hochreiter and Schmidhuber, 1997) cell for word , is the embedding for word , is the number of words in the query and is the vocabulary size. In this case, the hidden state summarizes all information of the sequence before . It is worth noting that two special tokens ¡sos¿ and ¡eos¿ are used, as illustrated in Figure 1.
However, this natural language modeling approach is inefficient. To compute the probability of a word , we need to compute the normalization term
. This term involves vector multiplication between the hidden stateand each word in the vocabulary. Usually the size of vocabulary can be larger than 30k, which produces a large computation overhead. Therefore, to reduce latency, approximation needs to be applied on the normalization term, similar to ”unnormalized” language modeling. Such approximation must satisfy several requirements:
Computational efficiency. The approximation should not require iterating every word over the vocabulary.
Ranking effectiveness. Under the approximation, although the absolute value of query probability will be affected, it shall preserve its relative ranking position w.r.t. other queries in most cases.
Length penalty. Long queries should receive more penalty than short ones, as short queries are more frequently typed.333The same pattern (short queries are more frequent) is observed in both AOL and LinkedIn datasets.
Therefore, we propose our design of unnormalized language modeling, where the normalization term is approximated by:
where is a scalar parameter to learn. Accordingly, equation 3.2 becomes:
This design satisfies the requirements: 1) For computational efficiency, since the approximation term is only a scalar value, latency is significantly reduced. 2) This design also keeps ranking effectiveness. More specifically, Equation 3 measures the semantic coherence of words in the query by , hence it assigns a meaningful ranking score for the query. 3) It also imposes length penalty by using a penalty term .
3.3. Comparison to State-of-the-Art Models
3.3.1. Comparison to CLSM Model
CLSM is used for candidate ranking in the previous work (Mitra and Craswell, 2015). This model applies Convolutional Neural Networks (CNN) (LeCun and Bengio, 1995) to learn an embedding for query prefix and suffix. The coherence score is the cosine similarity score between the prefix and suffix embedding. However, CNN only focuses on extracting n-gram features that is useful for ranking; it ignores the sequence length, and does not explicitly model word coherence . In our approach, we use neural language models that better model the coherence between words. We train the neural language model with a learning-to-rank layer in an end-to-end approach.
3.3.2. Comparison to an End-to-End Language Modeling Approach
The end-to-end language modeling approaches (Park and Chiba, 2017; Wang et al., 2018) yield great performance, since generation and ranking are performed at the same time for many iterations (one iteration for one generated token). However, it is also time-consuming. For a character level language model, ranking is required in every character generation, compared to the first-generate-then-rank framework.
3.3.3. Comparison to Existing Unnormalized Language Models
Different designs of unnormalized language models are proposed for machine translation (Devlin et al., 2014; Vaswani et al., 2013) and speech recognition (Sethy et al., ; Chen et al., 2015) to reduce computation time. In these applications, a valid probability is crucial for candidate generation. The first method is self normalization (Devlin et al., 2014; Sethy et al., ), which adds a regularization term to the task specific loss, to make sure the sum of probability of all words is close to 1. During decoding, the normalization term is dropped to accelerate speed, assuming it is equals to 1. However, the disadvantage is that training is slow, since the normalization term needs to be computed in training to obtain the loss.
Another popular method is Noise-Contrastive Estimation(Gutmann and Hyvärinen, 2010; Mnih and Teh, 2012; Vaswani et al., 2013; Chen et al., 2015). For each word,
noise words are added as negative examples. This method still requires a certain amount of the training time. Moreover, the normalization term is still computed at inference time and thus it does not meet the industrial latency requirement. In our case, our focus is the relative ranking of sentences (guided by the learning-to-rank loss) and both training and inference efficiency, rather than having an accurate estimation of the word probability. Therefore, we do not compare our model with Noise-Contrastive Estimation in this work.
4. Experiment Setup
4.1. Dataset Preparation
All experiments are conducted on the publicly available AOL dataset (Pass et al., 2006). Similar results are observed on the LinkedIn job search dataset.
The data preprocessing follows the previous work (Mitra and Craswell, 2015). We use the AOL data collected over three months (from March to June 2006) for experimentation. For preprocessing, we remove the empty query and keep only one copy of the adjacent identical queries. The dataset is split in the same way as in (Mitra and Craswell, 2015) – data from March 1 to April 30, 2006 are used as background data, with the following two weeks as training data, and each of the following two weeks as validation and test data. This results in 13.88 million background queries, 3.19 million training queries, 1.37 million validation queries, and 1.40 million test queries. For robustness, we exclude queries with frequency ¡ 3 in the background data.
Mapping from prefix to queries is constructed and stored in two FSTs, QueryFST and SuffixFST, as described in Section 3.1: QueryFST is built on the background data; SuffixFST is built on the most frequent 100k suffixes in the background data. No special treatment is applied on out-of-vocabulary words such as mapping them to unknown tokens. If a word cannot be found in FSTs, no suggestion will be made.
4.2. Baseline Models in Candidate Generation
In candidate generation, MostPopularCompletion (MPC) (Bar-Yossef and Kraus, 2011) and LastWordGeneration (LWG) (Mitra and Craswell, 2015) are used as baselines. These two methods are state-of-the-art methods that focus on usage of query prefixes and do not take in additional information such as personalization and time awareness.
MostPopularCompletion (MPC): Given a query prefix , this method searches for the most frequent ( is the number of candidates to be ranked in candidate ranking) queries starting with in QueryFST. QueryFST is built in the same way as in Section 4.1.
LastWordGeneration (LWG): Given a query prefix , this method first obtains historical queries starting with from QueryFST like MPC. After that, the last word (it could be an incomplete word) is extracted and the most frequent suffixes starting with are collected from SuffixFST. Extracted suffixes are prepended with (words in prefix except ) to make up a suggestion. Finally candidates from QueryFST and SuffixFST are merged together. QueryFST and SuffixFST are built in the same way as in Section 4.1.
4.3. Baseline Models in Candidate Ranking
In candidate ranking, we compare our unnormalized language model to two categories of models, frequency based models and neural network models.
4.3.1. Frequency based models
Frequency based models give higher ranks to more frequent candidates from MPC, LWG and MCG. For MPC, more frequent candidates are ranked higher. For LWG, candidates from the same FST are ranked by frequency. Candidates from QueryFST are ranked higher than those from SuffixFST. For MCG, the ranking is best described by Algorithm 1.
4.3.2. Neural network models
Given results from candidate generation, neural network models generate a score for each candidate and rank the candidates by their scores. We compare our model to the state-of-the-art Convolutional Latent Semantic Model (CLSM) (Shen et al., 2014) , and implement a simple LSTM model for comparison.
: Given a sequence and its corresponding embedding matrix, this model first extracts contextual features from n-grams using convolution filters and then extracts salient n-gram features using a max pooling layer. A semantic (dense) layer is then applied on the salient n-gram features to obtain a semantic vector for the sequence. Semantic vectors for both prefix and suggested suffix are extracted. Cosine similarity between these two vectors is computed and treated as the candidate score. In our experiments, all hyperparameters follow the same setting as in(Mitra and Craswell, 2015).
LSTMEmb: The basic architecture of this model is an LSTM network. Given a word sequence, each word is fed into the LSTM cell in order. The final hidden state vector is used as the semantic representation of the sequence. Given this semantic vector, dot product is computed between the semantic representation and a learnable weight vector and used as the ranking score.
For LSTMEmb and our unnormalized language model, we choose an embedding size of 100 as a balance of performance and speed. Xavier initialization (Glorot and Bengio, 2010) is used for embeddings. The hidden state size is set to be the same as the embedding size. The AdamWeightDecay (Loshchilov and Hutter, 2019) optimizer is used in training with a learning rate of and a weight decay rate of . The vocabulary size is 30k. Word hashing is not applied in vocabulary generation. Pairwise ranking is used with logistic loss in the learning-to-rank layer. The scoring model and the learning-to-rank model are jointly trained. The maximum size of a candidate list is set to 10 for each user input.
Model comparison is conducted in terms of relevance and efficiency. In candidate generation, We use recall to measure the performance of candidate generation methods because this metric measures the probability that users’ desired queries exist in the candidate list. More specifically, recall among top 10 results is measured.
In candidate ranking, we use mean reciprocal rank (MRR) to measure the relevance performance of each candidate ranking method. MRR is computed for the top 10 candidates. Similar to the previous work (Mitra and Craswell, 2015), MRR is calculated for two groups: 1) seen prefixes, prefixes that have matches in QueryFST and 2) unseen prefixes, prefixes that cannot find matches from QueryFST and therefore completed queries only come from SuffixFST.
Latency is used as the measure of candidate generation efficiency. The time cost of ranking a candidate list with 10 candidates is measured for each model. This is the average time cost over 1000 tests. The average number of words in candidates is 3.20.
5. Results on AOL
5.1. Candidate Generation
|Candidate Generation Methods||Recall@10|
indicates statistically significant improvements over LastWordGeneration through a paired t-test with p ¡ 0.05.
Table 2 compares the performance of different candidate generation methods, namely MPC, LWG and MCG. In consideration of efficiency, there’s a limit on the number of candidates to be generated and ranked. In our experiment, this limit is set to be 10. For each method, candidates are generated in the same order as the ranking order described in Section 4.3.1. Recall@10 is computed and its value is the same as the maximum MRR@10 that can be obtained in candidate ranking.
Our proposed candidate generation method MCG, has shown lift of Recall@10 over MPC and LWG both in total and in each prefix partition, with the major lift on unseen suffixes. In comparison with MPC, our methods provide a solution for rare and unseen suffixes, effectively leveraging context information of sequences following the first word of the prefix through the use of SuffixFST. In comparison with LWG, although technically MCG and LWG can produce the same candidate set, the candidate generation orders are different. Given the limit on the maximum number of candidates, this order has a large impact on QAC system performance. Our method prioritizes the generation of candidates that share more context with the user input. Therefore, MCG is able to exploit more context in user input to generate candidates with higher quality.
5.2. Candidate Ranking
|CLSM + Frequency||0.3369||0.4472||0.2610|
|LSTMEmbed +Frequency||0.3379 (+0.297%)||0.4472||0.2628|
|UnnormalizedLM +Frequency||0.3402 (+0.980%)||0.4473||0.2665|
|NormalizedLM +Frequency||0.3404 (+1.039%)||0.4473||0.2669|
Table 3 shows the performance of frequency based models, neural models and hybrid models. Frequency based models and neural models are described in Section 4.3. Hybrid models are the combination of neural and frequency based models, denoted by NN+Frequency in Table 3, where NN is a neural model. Among them, neural models only rank candidates from SuffixFST while keeping positions of candidates from QueryFST ranked by frequency.
As shown in the previous section, MCG exhibits the best candidate generation performance. Therefore, all neural ranking methods are performed on the candidates generated by MCG.
Consistent with results from the prior work (Park and Chiba, 2017), neural models that rank all candidates cannot outperform frequency-based methods on seen user inputs. This shows neural networks’ insufficiency in memorizing strong evidence. However, all neural networks exhibit lift on unseen user inputs, showing their power in evaluating the coherence of the query and the semantic relation between suggested suffixes and words not used by SuffixFST (i.e., words ”cheapest flights from” and the suffix ”seattle to vancouver” in Table 1).
Based on this observation, we conduct experiments for hybrid models. In these models, we keep the ranking of candidates from QueryFST given by frequency-based methods and apply neural networks only on candidates from SuffixFST. Results show that such combination achieves the best performance, with lift not only on unseen user inputs but also on seen user inputs. Note that the lift on seen user inputs comes from the fact that for seen user inputs, there are also results from SuffixFST when QueryFST provides less than 10 results.
Among results from neural models except normalized neural language models, our neural unnormalized language model performs the best both with and without the combination with frequency-based methods. This gain comes mainly from the design of language modeling, a more effective context modeling architecture.
The latency of MCG and neural ranking methods is shown in Table 4. The time cost of MCG is almost negligible because humans generally cannot notice latency in sub-millisecond level in auto completion. This shows the efficiency of MCG in capturing user context. Since the order of generated completions from MCG follows the order of frequency (high to low), the frequency based method with MCG generation has the same latency as MCG. Therefore, the major time cost of our two-step QAC system comes from neural ranking.
Compared with CLSM, our proposed unnormalized language model takes slightly more time, but it is still at the same scale. The extra time cost comes from the sequential dependency of LSTM cell outputs – output of each LSTM cell depends on the state of the previous cell. This is not an issue for CNN as the convolution can be done in parallel. Under the current setting of candidate number and length, our model only takes 3 ms to rank candidates, indicating the unnormalized model is efficient for industrial applications.
To get a sense of the speed of normalized neural language modeling, we also implement a normalized neural language model. This model computes the exact conditional probabilities of word occurrences without any approximation. Results show that a normalized neural language model takes 17 more times than an unnormalized language model. An average latency cost of 53.32ms for model inference is hardly acceptable for an auto completion system. For a fully generation-based neural language model with beam search, the latency will be further increased by a factor of beam search size.
6. Results on LinkedIn Production System
We apply the model on LinkedIn job search dataset, and conduct A/B tests with 50% traffic for two weeks. In the online system, users will receive QAC suggestions while typing. A list of job postings will be retrieved after user queries are submitted. Users can then view the postings and apply for jobs.
Figure 2 investigates the impact of the embedding size/hidden size and the layer number of LSTM on model inference speed and relevance performance. Even with the embedding size and the hidden vector size up to 500, the latency of our model is still lower than half of the latency of a normalized language model. Therefore, compared to normalized language models, under the same latency requirement, our unnormalized language modeling design can support more advanced models like Transformers (Vaswani et al., 2017) which have a better context understanding capability.
|Job Apply Clicks||+1.45%|
Dataset Preparation: We use one-month click through data from the LinkedIn job search engine to conduct experiments. The data splitting (background, training, validation and test data) and FST construction are done in the same way as that in AOL.
The baseline system follows a two-step ranking framework, with MCG as the candidate generation method and XGBoost(Chen and Guestrin, 2016) as the candidate ranking method. Multiple features are included in the XGBoost ranking model, such as frequency of suggested queries from background query logs, a Kneser–Ney smoothing language model score of the suggested queries, etc.
Metrics: We measure the performance in two aspects: (1) the impact on query auto completion component, by QAC CTR@5 (the click through rate of the top 5 suggested completions); (2) the impact on the overall job search results, by Job Views (number of jobs that users view through job search) and Job Apply Clicks (number of jobs that users apply through job search).
Online Results: We performA/B tests between model UnnormalizedLM +Frequency and the baseline system as shown in Table 5. Since the baseline system uses MCG in candidate generation as well, the focus of online experiments is on comparing the performance of a hand-crafted feature based model to an unnormalized neural language model. The QAC CTR@5 metric lift indicates that the quality of query auto completion is improved. The Job Views/Job Apply Clicks metric lifts show that more relevant job postings are retrieved because more meaningful queries are issued. The model is ramped to 100% traffic at LinkedIn’s English job search.
7. Conclusions and Future Work
In this paper, we propose an efficient neural QAC system that captures contextual information in both candidate generation and ranking given general logs. Our method is orthogonal to models that use personalized information such as user search history.
On the candidate generation side, more context words in query prefixes are utilized, resulting in more relevant candidates, especially for unseen prefixes. On the candidate ranking side, an unnormalized language model is proposed, which enables real-time deep semantic understanding of queries.
Besides its success in offline experiments, this system has been applied on the LinkedIn platform with great success. This technology not only saves user effort by suggesting queries related to users’ intent, but also helps users better reach their goals by providing queries that are more likely to retrieve desirable results.
In the future, we would like to explore acceleration techniques like (Wang et al., 2018). This direction is of great importance because it enables more advanced NLP techniques in an industrial QAC system. E.g., normalized neural language generation, an end-to-end QAC system with high recall (Park and Chiba, 2017), can be productionized. More advanced semantics encoders such as BERT (Devlin et al., 2018) can be used as well.
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