LDA-LFM: A Joint Exploitation of Review Text and Ratings in Recommender Systems

TK Aslanyan, Flavius Frasincar

Research output: Chapter/Conference proceedingConference proceedingAcademicpeer-review

Abstract

Most of the existing recommender systems are based only on the rating data, and they ignore other sources of information that might increase the quality of recommendations, such as textual reviews, or user and item characteristics. Moreover, the majority of those systems are applicable only on small datasets (with thousands of observations) and are unable to handle large datasets (with millions of observations). We propose a recommender algorithm that combines a rating modelling technique (i.e., Latent Factor Model) with a topic modelling method based on textual reviews (i.e., Latent Dirichlet Allocation), and we extend the algorithm such that it allows adding extra user- and item-specific information to the system. We evaluate the performance of the algorithm using Amazon.com datasets with different sizes, corresponding to 23 product categories. After comparing the built model to four other models we found that combining textual reviews with ratings leads to better recommendations. Moreover, we found that adding extra user and item features to the model increases its prediction accuracy, which is especially true for medium and large datasets.

Original languageEnglish
Title of host publicationProceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021
Pages1931-1940
Number of pages10
ISBN (Electronic)9781450381048
DOIs
Publication statusPublished - 22 Mar 2021

Fingerprint

Dive into the research topics of 'LDA-LFM: A Joint Exploitation of Review Text and Ratings in Recommender Systems'. Together they form a unique fingerprint.

Cite this