CD-CARS: Cross-domain context-aware recommender systems

Véras, Douglas; Prudêncio, Ricardo B. C.; Ferraz, Carlos

doi:10.1016/j.eswa.2019.06.020

Cited by 18 publications

(11 citation statements)

References 36 publications

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“…These methods take different approaches to model texts, i.e., corresponding to probabilistic generative processes and geometrically linear combinations, respectively. Therefore, in Figure 5 we compare the result to determine which is better for different topic numbers (5,10,15,20,50) for both methods corresponding to the datasets, and the hyper parameters α and β in LDA are set to 1 and 0.01,respectively. The iterative parameters of NMF are set to 100 iterations.…”

Section: E Experimental Resultsmentioning

confidence: 99%

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Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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Recommender systems are efficient tools for online applications; these systems exploit historical user ratings on items to make recommendations of items to users. This paper aims to enhance dynamic collaborative filtering on recommender systems under volatile conditions in which both users' preferences and item properties dynamically change over time. Moreover, existing collaborative filtering models mainly rely on solving data sparsity by adding side information to improve performance. We propose a model to capture the user preference dynamics in the rating matrix by using a joint decomposition method to extract user latent transition patterns and combine latent factors together with the associated topic evolution of review texts by using topic modeling based on the dynamic environment. We evaluate the accuracy on real datasets, and the experimental results show that the model leads to a significant improvement compared with the state-of-the-art dynamic CF models.

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Section: E Experimental Resultsmentioning

confidence: 99%

“…However, it is widely known that CF approaches suffer from a data sparsity problem [39], [41], [50]. Recently, a few temporal dynamic models have considered this problem.…”

Section: A Dynamic Collaborative Filteringmentioning

confidence: 99%

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Wangwatcharakul

Wongthanavasu

2020

IEEE Access

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“…In this work, we applied a re-ranking approach using a linear regression model on the recommendation list for next item prediction. Re-ranking approaches have been applied in different works [21]- [23], [25], [27], [38] where context-awareness, diversity and popularity based re-ranking options have been used. In this work, we applied a linear regression model to re-rank given the candidate recommendation list, and predict an interest level for recommended items based on multiple factors such as users' behaviours in users' previous sessions and recommended items' features.…”

Section: Conclusion and Future Directionmentioning

confidence: 99%

Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking

Esmeli

Bader-El-Den

Abdullahi

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Due to the increase in the importance of giving real-time recommendation to e-commerce users, session-based recommender systems become more popular. Session-based recommendation systems have the ability to adapt quickly to respond to changes in user interests and newly added items. The ranking is the core part of recommender systems regardless of recommender system type. Re-ranking is applied to recommender systems to have more personalised recommendations by considering contextawareness. In this paper, we proposed an approach to re-rank recommended items by using a linear regression model. In our approach, we use users' current session features and temporal features of recommended items to measure a user's interest level on a recommended item. We focus on having better recall and precision scores with fewer recommendations to able to prove the success of our re-ranking strategy. We conduct computational experiments on six real-world datasets and show that after applying re-ranking, we can get higher recall and precision scores. These results confirm that taking user interest level on an item in a session into account can improve the chance of getting correct items in top 5 recommendations.

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“…With the continuous expansion of the Internet, information is rapidly growing at an explosive rate. Excessive information appears in front of users, making it impossible for users to distinguish and obtain effective information [1]- [3]. Recommender systems are regarded as an essential measure to solve this problem by analyzing the historical data and predicting the user's interest in the items [4].…”

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confidence: 99%

A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Recommender Systems

Zhang

2020

IEEE Access

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In real-world recommender systems, users' interest and products' characteristics tend to go through a distinct series of changes over time. Thus, designing a recommender system that can simultaneously track the temporal dynamics of both drifts becomes a significant task. However, most of the existing temporal recommender systems only focus on users' dynamics, ignoring changes in products' characteristics. In this study, we propose a Multi-Trans matrix factorization (MTMF) model with improved time weight to capture temporal dynamics. Firstly, we introduce a personalized time weight that combines the forgetting curve and item similarity to reduce the impact of outdated information and retain the influence of users' stable preferences. Then, we model user and item dynamics by learning the multiple transitions at the userfactor and factor-item latent space between the ongoing time period and all past time periods. Accordingly, we formulate a joint objective function and take a gradient-based alternating optimization algorithm to solve this joint problem. Experimental results on historical datasets MovieLens show that the recommendation accuracy of MTMF with improved time weight is superior to the existing temporal recommendation methods. INDEX TERMS Recommender systems, collaborative filtering, time weight, dynamic preference. I. INTRODUCTION

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CD-CARS: Cross-domain context-aware recommender systems

Cited by 18 publications

References 36 publications

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Dynamic Collaborative Filtering Based on User Preference Drift and Topic Evolution

Improving Session-Based Recommendation Adopting Linear Regression-Based Re-ranking

A Multi-Trans Matrix Factorization Model With Improved Time Weight in Temporal Recommender Systems

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