A Novel Recommendation Algorithm Incorporating Temporal Dynamics, Reviews and Item Correlation

Abstract

SUMMARY Recommender systems (RS) exploit user ratings on items and side information to make personalized recommendations. In order to recommend the right products to users, RS must accurately model the implicit preferences of each user and the properties of each product. In reality, both user preferences and item properties are changing dynamically over time, so treating the historical decisions of a user or the received comments of an item as static is inappropriate. Besides, the review text accompanied with a rating score can help us to understand why a user likes or dislikes an item, so temporal dynamics and text information in reviews are important side information for recommender systems. Moreover, compared with the large number of available items, the number of items a user can buy is very limited, which is called the sparsity problem. In order to solve this problem, utilizing item correlation provides a promising solution. Although famous methods like TimeSVD++, TopicMF and CoFactor partially take temporal dynamics, reviews and correlation into consideration, none of them combine these information together for accurate recommendation. Therefore, in this paper we propose a novel combined model called TmRevCo which is based on matrix factorization. Our model combines the dynamic user factor of TimeSVD++ with the hidden topic of each review text mined by the topic model of TopicMF through a new transformation function. Meanwhile, to support our five-scoring datasets, we use a more appropriate item correlation measure in CoFactor and associate the item factors of CoFactor with that of matrix factorization. Our model comprehensively combines the temporal dynamics, review information and item correlation simultaneously. Experimental results on three real-world datasets show that our proposed model leads to significant improvement compared with the baseline methods.

Recommender System Based on Temporal Models: A Systematic Review

“…For such purposes, both matrix and tensor factorizations have been modified as time-dependent and time-independent models [26]. In a time-dependent approach, the time information is not necessarily used in the models, hence the timestamps are not strictly required [127][128][129][130]. Instead, the rating data is approached as an ordered sequence as a time series problem.…”

“…Time-Dependent tensor models were also exploited in the literature where time is not explicitly defined in the model [73,74,[129][130][131]. For example, [66] proposed a tensor-based method by using timestamped data with different periodic patterns.…”

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

“…Temporal collective matrix factorization (TCMF) [42] was proposed considering both temporal dynamics and multimodal information for addressing data sparsity, but they exploit only implicit feedback of user comments and ignore the hidden meaning of additional information. Moreover, TMRevCo [55] considers temporal dynamics and side information. This approach uses a more appropriate item correlation measure in the cofactor and associates the item factors of the cofactor with that of MF, but it focuses on item correlation dimensions, which is a different method than our work.…”

“…The model is based on matrix factorization model that factorizes rating matrix into latent user and item factors for rating prediction. The model focus on dynamic user factor and item correlation measure in CoFactor and associate the item factors of CoFactor with that of matrix factorization [55]. All these approaches are tested in the same experimental environment.…”

“…There are a few approaches that have handled user preference drift and other subproblems, such as data sparsity [29], [30], [42], [55]. Recently, OCF-DR [29] use neighborhood factor to track the drift of users' preference.…”