Movie collaborative filtering with multiplex implicit feedbacks

doi:10.1016/j.neucom.2019.03.098

Neurocomputing

Volume 398, 20 July 2020, Pages 485-494

https://doi.org/10.1016/j.neucom.2019.03.098 Get rights and content

Abstract

Movie recommender systems have been widely used in a variety of online networking platforms to give users reasonable advice from a large number of choices. As a representative method of movie recommendation, collaborative filtering uses explicit and implicit feedbacks to mine users’ preferences. The use of implicit features can help to improve the accuracy of movie collaborative filtering. However, multiplex implicit feedbacks have not been investigated and utilized comprehensively. In this paper, we analyze different kinds of implicit feedbacks in movie recommendation, including user similarities for movie tastes, rated records of each movie and positive attitude of users, and incorporate these feedbacks for collaborative filtering. User relationships are extracted according to user similarities. We propose a recommendation method with multiplex implicit feedbacks (RMIF), which factorizes both the explicit rating matrix and implicit attitude matrix. To demonstrate the effectiveness of our method, we conduct extensive experiments on two real datasets. Experiment results prove that RMIF significantly outperforms state-of-the-art models in terms of accuracy. Among different kinds of implicit feedbacks, positive attitude has the most important role in movie collaborative filtering.

Introduction

With the development of multimedia technology, various types of movies and videos on social media are exploding, making it much difficult for online users to find useful video information. Collaborative filtering is an effective solution to the problem of information overload, and helps users to discover the videos in which they are interested. Therefore, personalized recommender systems have become one of the most popular applications since the mid-90s [1]. Varian and Resnick gave the formal definition of recommender systems in 1997, which use e-commerce sites to provide product information and advice for customers, to help users decide which products to buy, and to help customers to complete the purchase [2]. In recent years, recommender systems have been widely concerned by researchers, and many recommendation models have been presented. The task of recommender systems is mainly divided into two categories: item recommendation and rating prediction. Item recommendation predicts a set of items that users have great probability to adopt. Rating prediction estimates ratings on items that users have not given, and is often used in movie sharing websites [3]. The goal of this paper is rating prediction.

Collaborative filtering (CF) is one of the most popular methods to implement a recommender system. The idea of CF is that users who have similar tastes in the past are tending to choose the same items [4], [5]. Matrix factorization (MF) is a model-based CF, which has been widely used. MF uses the multiplication of two low-rank feature matrices to represent the rating matrix and fill in the gap values in the matrix [6]. However, most of CF methods have a common shortcoming that when rating data are sparse, the accuracy of predicted results may be greatly reduced [7]. Recommender systems address three objects: users, items, and explicit ratings on items. In fact, implicit information that can be explored from explicit data is also advantageous for recommendation. Therefore, a feasible way to solve the problem of data sparsity is to mine various information from original data. Nowadays, recommender systems can also combine many advanced methods in other fields, such as hybrid feedback control technique [8], static output feedback stabilization [9], and robust adaptive neural control [10].

In general, the information we can obtain in recommender systems contains explicit feedbacks and implicit feedbacks. Explicit feedbacks refer to the data which are obtained through users’ directly actions, including ratings provided by users, tags of movies, and attributes of users such as the gender, age, and so on. In contrast to explicit feedbacks, implicit feedbacks are extracted based on the traces of usage left by users after they have interacted on a website. These feedbacks include browsing records, user similarities, favorite collections, etc. In addition, we can use the information retrieved from the images of items as a kind of implicit feedback [11], [12], [13]. There are many methods of image processing, such as image re-ranking [14]. From the perspective of data sparsity, it is obvious that traces of usage are much more abundant than direct actions. Therefore, implicit feedbacks may contribute to recommendation in addition to explicit feedbacks. Designing CF methods based on implicit feedbacks can alleviate the problem of data sparsity and improve the accuracy and stability of recommender systems.

There are many sources of implicit feedbacks, so it is essential to choose the appropriate and effective feedbacks to design a CF algorithm. However, most of previous studies used a certain kind of feedback separately, and did not combine different feedbacks together. In this paper, we present a novel recommendation method based on multiplex implicit feedbacks, called RMIF. RMIF integrates different kinds of implicit feedbacks into the MF framework. In this paper, we mainly consider user similarities, rated records of items and positive attitude of users as implicit feedbacks. The rated records of each item refer to the set of users who have rated the item. After users observe others’ ratings on an item, their decisions on the same item may be influenced. The rated records of each item have implicit influence on the users who will rate the item. Implicit feedbacks reveal additional information about users’ actions, so users’ preferences can be portrayed more realistically. Mining users’ actions comprehensively and combining a variety of implicit feedbacks together may help to improve recommendation. To prove the reliability and effectiveness of RMIF, we conduct extensive experiments on two real-world datasets. Results prove that incorporating implicit feedbacks significantly improves the accuracy of recommendation compared with state-of-the-art CF methods. Our work has the following contributions:

(1)
We introduce three kinds of implicit feedbacks from the rating matrix separately in detail. We explore the feedbacks of user similarities and items’ rated records to improve rating prediction. In addition, we use the positive attitude of users as an auxiliary feedback to make prediction more accurate.
(2)
We propose a novel CF method based on multiplex implicit feedbacks. We incorporate these implicit feedbacks into the MF framework. The feedbacks of user similarities and positive attitude of users are used in the feature space of users. Rated records of items are used in the feature space of items. By constructing the feature spaces, we use MF to predict unknown ratings.
(3)
We conduct extensive experiments on two datasets to verify the performance of RMIF. Compared with some state-of-the-art recommendation models, our method significantly improves the recommendation accuracy. In addition, we analyze the impact of each implicit feedback on the performance, and prove that the feedbacks we use are reasonable.

The rest of this paper is organized as follows. Section 2 provides a brief overview of some related work. Then, we describe the proposed method in detail in Section 3, and design experiments to evaluate the effectiveness of our method in Section 4. Finally, we give some concluding remarks and future directions in Section 5.

Section snippets

Related work

Matrix factorization is an effective model-based CF approach. From a mathematical view, the goal of MF is to approximate the rating matrix by constructing two low-rank matrices that have interactions in latent feature spaces [15]. We assume that there are m users and n items in a recommender system. Each user is defined as $u \in {1, 2, \dots, m}$ and each item is defined as $j \in {1, 2, \dots, n}$ . Then, the user-item rating matrix R_m × n can be used to represent users’ opinions about items. The element r_uj in the

Recommendation with multiplex implicit feedbacks

In this section, first, we introduce the problem we pay attention to in this paper and the overall framework of RMIF. Then, we describe three kinds of implicit feedbacks in RMIF respectively, and introduce their roles in recommendation. Finally, the algorithm of RMIF is given in detail.

Datasets and evaluation metrics

In the experiments, we select the Movielens-100k (ML-100k) dataset released by GroupLens Lab. To verify the feasibility and robustness of the algorithm, we also extend our experiments to the larger Movielens-1 M (ML-1 m) dataset for retesting and verification [31]. Data in both datasets are recorded in the form of (u,j,r_uj), and all ratings are expressed by five-level integers (1–5). We construct the auxiliary binary matrix of users’ attitude by converting the ratings larger than or equal to 3

Conclusion

In contrast to explicit ratings, implicit information can describe users’ preferences more realistic, and helps to solve the problem of data sparsity in recommendation. In this paper, we explored three kinds of implicit feedbacks through our observation: user similarities, rated records of items and users’ positive attitude. To make rating prediction, we proposed a recommendation method which incorporates multiplex implicit feedbacks. We introduced three implicit feedbacks into the matrix

Declarations of Competing Interest

None.

Acknowledgments

This work has been supported by the National Natural Science Foundation of China under Grant 61872033, the Humanity and Social Science Youth Foundation of Ministry of Education of China under Grant 18YJCZH204 and 17YJCZH007, the Fundamental Research Funds for the Central Universities in UIBE under Grant CXTD10-05, the Research Funds for Excellent Young Scholars in UIBE under Grant 17YQ21, and the Beijing Natural Science Foundation under Grant 4184084.

Yutian Hu received the B.E. degree in communication and information systems from Beijing Jiaotong University, Beijing, China, in 2018. From 2016 to 2017, she was an exchange student at Taiwan National Central University. She is currently a master student with the School of Electronic and Information Engineering, Beijing Jiaotong University. Her current research is mainly on recommender system.

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Fei Xiong received the B.E. degree and the Ph.D. degree in communication and information systems from Beijing Jiaotong University, Beijing, China, in 2007 and 2013. He is currently an Associate Professor with the School of Electronic and Information Engineering, Beijing Jiaotong University. From 2011 to 2012, he was a visiting scholar at Carnegie Mellon University. He has published over 60 papers in refereed journals and conference proceedings. He was a recipient of National Natural Science Foundations of China and several other research grants. His current research interests include the areas of web mining, complex networks and complex systems.

Dongyuan Lu received the B.S. degree from Beijing Normal University, Beijing, China, in 2007, and the Ph.D. degree from CASIA in 2012. She is currently an Associate Professor in School of Information Technology and Management, University of International Business and Economics. Her research interests include social media analysis, information retrieval and data mining.

Ximeng Wang received the B.E. degree in communication engineering from the Nanjing University of Posts and Telecommunications, Nanjing, China, in 2011, and the M.E. degree in software engineering from Beijing Jiaotong University, Beijing, China, in 2013. He is currently pursuing the Ph.D. degree with Beijing Jiaotong University. Since 2017, he has been a joint Ph.D. student with the University of Technology Sydney. His current research interests include recommender systems, complex networks and data mining.

Xi Xiong received the B.S. and M.S. degrees from the Beijing Institute of Technology and the Ph.D. degree in information security from Sichuan University, Chengdu, in 2013. He is currently a lecturer with the School of Cybersecurity, Chengdu University of Information Technology, Chengdu, China. He has published over 20 papers in the most prestigious journals and conferences. His research interests include web mining, social computing and machine learning.

Hongshu Chen received the dual Ph.D. degrees in Management Science and Engineering from Beijing Institute of Technology, Beijing, China, in 2015, and in Software Engineering from University of Technology Sydney, NSW, Australia, in 2016. She is currently a Lecturer with the School of Management and Economics, Beijing Institute of Technology. Her research interests include bibliometrics, information systems and text analytics.

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Movie collaborative filtering with multiplex implicit feedbacks

Abstract

Introduction

Section snippets

Related work

Recommendation with multiplex implicit feedbacks

Datasets and evaluation metrics

Conclusion

Declarations of Competing Interest

Acknowledgments

Neurocomputing

Neurocomputing

Knowl. Based Syst.

Neurocomputing

Neurocomputing

Neurocomputing

Knowl. Based Syst.

Knowl.-Based Syst.

Neurocomputing

Neurocomputing

Recommender systems

Commun. ACM

A novel recommendation model regularized with user trust and item ratings

IEEE Trans. Knowl. Data Eng.

Item-based top-n recommendation algorithms

ACM Trans. Inf. Syst.

Matrix factorization techniques for recommender systems

Computer (Long Beach Calif)

Sampled-data static output-feedback control for nonlinear systems in T-S form via descriptor redundancy

Neurocomputing

Deep multimodal distance metric learning using click constraints for image ranking

IEEE Trans. Cybern.

Learning to rank using user clicks and visual features for image retrieval

IEEE Trans. Cybern.

Click prediction for web image reranking using multimodal sparse coding

IEEE Trans. Image Process.

Exploiting click constraints and multiview features for image reranking

IEEE Trans. Multimed.

Matrix factorization recommendation algorithm based on spark

J. Comput. Appl.