User-Service Rating Prediction by Exploring Social Users’ Rating Behaviors
With the boom of social media, it is a very popular trend for people to share what they are doing with friends across various social networking platforms. Nowadays, we have a vast amount of descriptions, comments, and ratings for local services. The information is valuable for new users to judge whether the services meet their requirements before partaking.
In this project, we propose a user-service rating prediction approach by exploring social users’ rating behaviors. In order to predict user-service ratings, we focus on users’ rating behaviors.
In our opinion, the rating behavior in recommender system could be embodied in these aspects: 1) when user rated the item, 2) what the rating is, 3) what the item is, 4) what the user interest that we could dig from his/her rating records is, and 5) how the user’s rating behavior diffuses among his/her social friends. Therefore, we propose a concept of the rating schedule to represent users’ daily rating behaviors. In addition, we propose the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors.
In the proposed user-service rating prediction approach, we fuse four factors—user personal interest (related to user and the item’s topics), interpersonal interest similarity (related to user interest), interpersonal rating behavior similarity (related to users’ rating behavior habits), and interpersonal rating behavior diffusion (related to users’ behavior diffusions)—into a unified matrix-factorized framework. We conduct a series of experiments in the Yelp dataset and Douban Movie dataset. Experimental results show the effectiveness of our approach.
Many models based on social networks have been proposed to improve recommender system performance. The concept of ‘inferred trust circle’ based on circles of friends was proposed by Yang et al. to recommend favorite and useful items to users. Their approach, called the CircleCon Model, not only reduces the load of big data and computation complexity, but also defines the interpersonal trust in the complex social networks.
Chen et al. propose to conduct personalized travel recommendation by taking user attributes and social information.
Most recent work has followed the two aforementioned directions (i.e., user-based and itembased).
Herlocker et al. propose the similarity between users or items according to the number of common ratings.
Deshpande and Karypis apply an item-based CF combined with a condition-based probability similarity and Cosine Similarity.
Collaborative filtering-based recommendation approaches can be viewed as the first generation of recommender system.
DISADVANTAGES OF EXISTING SYSTEM:
Unsuitable for real life applications because of the increased computational and communication costs.
No Secure computation of recommendation.
In this project, we propose a user-service rating prediction model based on probabilistic matrix factorization by exploring rating behaviors. Usually, users are likely to participate in services in which they are interested and enjoy sharing experiences with their friends by description and rating.
we propose a user-service rating prediction approach by exploring social users’ rating behaviors in a unified matrix factorization framework.
The main contributions of this paper are shown as follows.
We propose a concept of the rating schedule to represent user daily rating behavior. We leverage the similarity between user rating schedules to represent interpersonal rating behavior similarity.
We propose the factor of interpersonal rating behavior diffusion to deep understand users’ rating behaviors. We explore the user’s social circle, and split the social network into three components, direct friends, mutual friends, and the indirect friends, to deep understand social users’ rating behavior diffusions.
We fuse four factors, personal interest, interpersonal interest similarity, interpersonal rating behavior similarity, and interpersonal rating behavior diffusion, into matrix factorization with fully exploring user rating behaviors to predict user-service ratings. We propose to directly fuse interpersonal factors together to constrain user’s latent features, which can reduce the time complexity of our model.
ADVANTAGES OF PROPOSED SYSTEM:
The proposed system focus on exploring user rating behaviors. A concept of the rating schedule is proposed to represent user daily rating behavior. The factor of interpersonal rating behavior diffusion is proposed to deep understand users’ rating behaviors. The proposed system considers these two factors to explore users’ rating behaviors.
The proposed system fuse three factors, interpersonal interest similarity, interpersonal rating behavior similarity, and interpersonal rating behavior diffusion, together to directly constrain users’ latent features, which can reduce the time complexity.
System : Pentium Dual Core.
Hard Disk : 120 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 1GB.
Operating system : Windows 7.
Coding Language : C#,.NET
Database : SQL
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “Grouplens: an open architecture for collaborative filtering of netnews,” in CSCW ’94, 1994, pages 175–186.