Deep Matrix Factorization Approach for Collaborative Filtering Recommender Systems

On Exploiting Rating Prediction Accuracy Features in Dense Collaborative Filtering Datasets

One of the typical goals of collaborative filtering algorithms is to produce rating predictions with values very close to what real users would give to an item. Afterward, the items having the largest rating prediction values will be recommended to the users by the recommender system. Collaborative filtering algorithms can be applied to both sparse and dense datasets, and each of these dataset categories involves different kinds of risks. As far as the dense collaborative filtering datasets are concerned, where the rating prediction coverage is, most of the time, very high, we usually face large rating prediction times, issues concerning the selection of a user’s near neighbours, etc. Although collaborative filtering algorithms usually achieve better results when applied to dense datasets, there is still room for improvement, since in many cases, the rating prediction error is relatively high, which leads to unsuccessful recommendations and hence to recommender system unreliability. In this work, we explore rating prediction accuracy features, although in a broader context, in dense collaborative filtering datasets. We conduct an extensive evaluation, using dense datasets, widely used in collaborative filtering research, in order to find the associations between these features and the rating prediction accuracy.

On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets

The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user–item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system’s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.

Improving Graph-Based Movie Recommender System Using Cinematic Experience

With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.

A Closer-to-Reality Model for Comparing Relevant Dimensions of Recommender Systems, with Application to Novelty

Providing fair and convenient comparisons between recommendation algorithms—where algorithms could focus on a traditional dimension (accuracy) and/or less traditional ones (e.g., novelty, diversity, serendipity, etc.)—is a key challenge in the recent developments of recommender systems. This paper focuses on novelty and presents a new, closer-to-reality model for evaluating the quality of a recommendation algorithm by reducing the popularity bias inherent in traditional training/test set evaluation frameworks, which are biased by the dominance of popular items and their inherent features. In the suggested model, each interaction has a probability of being included in the test set that randomly depends on a specific feature related to the focused dimension (novelty in this work). The goal of this paper is to reconcile, in terms of evaluation (and therefore comparison), the accuracy and novelty dimensions of recommendation algorithms, leading to a more realistic comparison of their performance. The results obtained from two well-known datasets show the evolution of the behavior of state-of-the-art ranking algorithms when novelty is progressively, and fairly, given more importance in the evaluation procedure, and could lead to potential changes in the decision processes of organizations involving recommender systems.