NAIS: Neural Attentive Item Similarity Model for Recommendation

09/19/2018
by   Xiangnan He, et al.
0

Item-to-item collaborative filtering (aka. item-based CF) has been long used for building recommender systems in industrial settings, owing to its interpretability and efficiency in real-time personalization. It builds a user's profile as her historically interacted items, recommending new items that are similar to the user's profile. As such, the key to an item-based CF method is in the estimation of item similarities. Early approaches use statistical measures such as cosine similarity and Pearson coefficient to estimate item similarities, which are less accurate since they lack tailored optimization for the recommendation task. In recent years, several works attempt to learn item similarities from data, by expressing the similarity as an underlying model and estimating model parameters by optimizing a recommendation-aware objective function. While extensive efforts have been made to use shallow linear models for learning item similarities, there has been relatively less work exploring nonlinear neural network models for item-based CF. In this work, we propose a neural network model named Neural Attentive Item Similarity model (NAIS) for item-based CF. The key to our design of NAIS is an attention network, which is capable of distinguishing which historical items in a user profile are more important for a prediction. Compared to the state-of-the-art item-based CF method Factored Item Similarity Model (FISM), our NAIS has stronger representation power with only a few additional parameters brought by the attention network. Extensive experiments on two public benchmarks demonstrate the effectiveness of NAIS. This work is the first attempt that designs neural network models for item-based CF, opening up new research possibilities for future developments of neural recommender systems.

READ FULL TEXT
research
02/22/2021

Factor-level Attentive ICF for Recommendation

Item-based collaborative filtering (ICF) enjoys the advantages of high r...
research
11/11/2018

Deep Item-based Collaborative Filtering for Top-N Recommendation

Item-based Collaborative Filtering(short for ICF) has been widely adopte...
research
05/14/2022

PAS: A Position-Aware Similarity Measurement for Sequential Recommendation

The common item-based collaborative filtering framework becomes a typica...
research
04/26/2021

Represent Items by Items: An Enhanced Representation of the Target Item for Recommendation

Item-based collaborative filtering (ICF) has been widely used in industr...
research
07/01/2022

Modelling Users with Item Metadata for Explainable and Interactive Recommendation

Recommender systems are used in many different applications and contexts...
research
02/17/2019

Collaborative Similarity Embedding for Recommender Systems

We present collaborative similarity embedding (CSE), a unified framework...
research
05/10/2022

Bayesian Prior Learning via Neural Networks for Next-item Recommendation

Next-item prediction is a a popular problem in the recommender systems d...

Please sign up or login with your details

Forgot password? Click here to reset