Elsevier

Information Fusion

Volume 46, March 2019, Pages 141-146
Information Fusion

EARS: Emotion-aware recommender system based on hybrid information fusion

https://doi.org/10.1016/j.inffus.2018.06.004Get rights and content

Highlights

  • An emotion-aware recommender system based on hybrid information fusion, improving the quality of recommender services.

  • Three representative kinds of information being fused to comprehensively analyze user’s feature.

  • Higher predication rating and recommendation accuracy than conventional approaches.

Abstract

Recommender systems suggest items that users might like according to their explicit and implicit feedback information, such as ratings, reviews, and clicks. However, most recommender systems focus mainly on the relationships between items and the user’s final purchasing behavior while ignoring the user’s emotional changes, which play an essential role in consumption activity. To address the challenge of improving the quality of recommender services, this paper proposes an emotion-aware recommender system based on hybrid information fusion in which three representative types of information are fused to comprehensively analyze the user’s features: user rating data as explicit information, user social network data as implicit information and sentiment from user reviews as emotional information. The experimental results verify that the proposed approach provides a higher prediction rating and significantly increases the recommendation accuracy.

Introduction

Information overload (information overload) is an increasing problem that cannot be ignored. Recommendation systems were developed to reduce the time that users spend browsing useless information. A recommendation system recommends interests and merchandise to the user by observing the user’s interest characteristics and selection behavior, and even provides personalized services [1]. In recent years, research on recommendation systems has expanded, and the huge amount of accompanying data has brought new challenges for recommendation systems.

With the development of big data, cloud computing, mobile computing and other advanced information technologies [2], many types of data are used in recommender systems, and the information that is advantageous to the system must be identified. Therefore, researchers have begun to integrate all types of information and make recommendations based on the fused information. However, the fused information comes from different dimensions, such as personal information, social information, emotional information and whether the activity area of users is explicit [3], [4]. Various works have proved that fusion information can significantly improve the availability of the recommendation system [5], [6].

The core resources that support the recommendation system are the user’s historical behavior data, including explicit feedback and implicit feedback [7]. Most explicit feedback-based collaborative filtering recommendation systems are based on user ratings and trust information to improve the accuracy of recommendation [8]. However, this will miss a lot of implicit feedback data. Implicit feedback data is more common than the additional inputs required for explicit feedback data, and its collection costs are low and do not affect the user experience. To address the shortcomings of explicit feedback data recommendation systems, recommendation systems based on implicit feedback data have been introduced. However, implicit feedback data can express the user’s positive feedback but are less able to express the user’s negative feedback. Solving the problem of the lack of negative feedback is thus very important.

At present, recommendation systems based on implicit feedback data face the following three challenges:

  • 1.

    Sample imbalance. In implicit feedback data, there are usually only positive feedback and no negative feedback. In contrast to explicit feedback data, which directly reflect the tendency of the user’s likes and dislikes, implicit data include “selected” and “non-selected” categories. Although the & quot; selected & quot; may indicate a user & apos; s tendency, the & quot; unselected & quot; cannot directly represent a user & apos; s negative tendency because “not selected” includes not only products in which the user is not really interested but also products in which the user is interested but has not yet found. This lack of positive examples adds difficulty to the model.

  • 2.

    Noise. In contrast to an explicit rating, in implicit feedback data, the user can produce a lot of noise due to various misuses.

  • 3.

    Large scale. The actual scenario of the recommendation model will involve large-scale data, which requires the model to have sufficiently efficient performance and excellent scalability to handle vast amounts of data.

In this paper, the model proposed in the above problem is used to model the implicit feedback data directly by converting the recommended task into the probability of maximizing the probability of user selection behavior. The model will be able to “unselect” the information that is fully utilized while avoiding the introduction of negative cases and noise to improve recommendation quality. Specifically, this article makes the following contributions: (1) it develops an effective approach to fuse social information, rating information and emotional information for recommender systems; (2) assisted by the hybrid features from implicit and explicit data, the performance of the proposed recommender system is significantly improved.

Section snippets

Recommender systems

Traditional recommendation systems are difficult to apply directly to implicit feedback data because the data contain only positive feedback from the user and lack negative feedback. In [9], Pan et al. defines this issue as a single type of collaborative filtering (One Class Collaborative Filtering, OCCF), which is generally summarized as an imbalance problem (Unbalanced Class Problem, UCP) [10]. The main way to deal with this problem is to introduce negative feedback in the following three

System architecture

Fig. 1 shows the architecture of the proposed recommender system, which consists of the following four components:

  • 1.

    Data Collection: In the proposed scheme, three types of raw data are collected for information fusion, i.e., social network data, rating data and user review data. With the development of crowd-sourced review websites such as Yelp,1 these data are easy to access.

  • 2.

    Information Fusion: Among the collected data, rating data are explicit information

Data set

In the experiment, the experimental datasets were obtained from watercress, a leading community site that offers audio book recommendation, city activities, and group exchanges under topic lines featuring a variety of services. This experiment considers a song book or product (Item). All information is collected to obtain the implicit feedback stream sorted by time. Item 383 grabs the user’s information from watercress online, including the historical behavior of their selection of books and

Conclusion

In this paper, an empirical model based on implicit feedback data is studied experimentally. First, the implicit feedback data stream is explained, and the potential feature implicit feedback recommendation model based on matrix decomposition is then given. The probability of the proposed problem is modeled as an optimization problem by maximizing the probability of the user’s selection behavior. The model is verified by a relevant data set, and the superiority of the model is verified by

Acknowledgments

This work was supported by the China National Natural Science Foundation under Grant 61702553 and the Project of Humanities and Social Sciences (17YJCZH252) funded by the China Ministry of Education (MOE).

References (26)

  • I. Palomares et al.

    Multi-view fuzzy information fusion in collaborative filtering recommender systems: application to the urban resilience domain

    Data Knowl. Eng.

    (2017)
  • H. Zhao et al.

    Meta-graph based recommendation fusion over heterogeneous information networks

    Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

    (2017)
  • X. He et al.

    Neural collaborative filtering

    Proceedings of the 26th International Conference on World Wide Web

    (2017)
  • Cited by (0)

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