A Generalized and Fast-converging Non-negative Latent Factor Model for Predicting User Preferences in Recommender Systems

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.

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Deep Latent Factor Model with Hierarchical Similarity Measure for recommender systems

Information Sciences ◽

10.1016/j.ins.2019.07.024 ◽

2019 ◽

Vol 503 ◽

pp. 521-532 ◽

Cited By ~ 4

Author(s):

Jiayu Han ◽

Lei Zheng ◽

He Huang ◽

Yuanbo Xu ◽

Philip S. Yu ◽

...

Keyword(s):

Recommender Systems ◽

Similarity Measure ◽

Factor Model ◽

Latent Factor ◽

Latent Factor Model

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Recommendation vs Sentiment Analysis: A Text-Driven Latent Factor Model for Rating Prediction with Cold-Start Awareness

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/382 ◽

2017 ◽

Cited By ~ 8

Author(s):

Kaisong Song ◽

Wei Gao ◽

Shi Feng ◽

Daling Wang ◽

Kam-Fai Wong ◽

...

Keyword(s):

Sentiment Analysis ◽

Factor Model ◽

Cold Start ◽

User Preferences ◽

Supplementary Information ◽

Latent Factor ◽

Latent Factor Model ◽

Rating Prediction ◽

The Difference ◽

Product Interaction

Review rating prediction is an important research topic. The problem was approached from either the perspective of recommender systems (RS) or that of sentiment analysis (SA). Recent SA research using deep neural networks (DNNs) has realized the importance of user and product interaction for better interpreting the sentiment of reviews. However, the complexity of DNN models in terms of the scale of parameters is very high, and the performance is not always satisfying especially when user-product interaction is sparse. In this paper, we propose a simple, extensible RS-based model, called Text-driven Latent Factor Model (TLFM), to capture the semantics of reviews, user preferences and product characteristics by jointly optimizing two components, a user-specific LFM and a product-specific LFM, each of which decomposes text into a specific low-dimension representation. Furthermore, we address the cold-start issue by developing a novel Pairwise Rating Comparison strategy (PRC), which utilizes the difference between ratings on common user/product as supplementary information to calibrate parameter estimation. Experiments conducted on IMDB and Yelp datasets validate the advantage of our approach over state-of-the-art baseline methods.

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