To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering

Abstract

Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users' behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect th e performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google's pre-trained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the kNN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.

An experimental study on the performance of collaborative filtering based on user reviews for large-scale datasets

“…Few researchers ( Da’u et al, 2020 ; Ray, Garain & Sarkar, 2021 ; AL-Ghuribi et al, 2023 ) have also considered using aspects to improve CF recommendations, as the aspects can provide better information about user preferences. However, the current studies that utilize aspects mainly concentrate on extracting aspects to represent user preferences instead of figuring out their implicit ratings ( Ray, Garain & Sarkar, 2021 ; Akhtar et al, 2017 ; Liu, Zhang & Gulla, 2021 ).…”

“…Other approaches that use user reviews go even further by proposing sentiment-based models that integrate contextual information to enhance the CF ( Osman et al, 2021 ) or generate user profiles to identify user preferences ( Cheng et al, 2019 ; Bauman, Liu & Tuzhilin, 2017 ; Wang, Wang & Xu, 2018 ). Moreover, others like ( Da’u et al, 2020 ; Ray, Garain & Sarkar, 2021 ; AL-Ghuribi et al, 2023 ; Cheng et al, 2018 ) focused on integrating aspect/feature elements of reviews into CF as multiple criteria to improve the CF performance. As can be seen from the current studies, little emphasis has been placed on using user reviews to determine the implicit rating and integrate it into the CF approach.…”