Guoshuai Zhao

Personalized Recommendation Combining User Interest and Social Circle

With the advent and popularity of social network, more and more users like to share their experiences, such as ratings, reviews, and blogs. The new factors of social network like interpersonal influence and interest based on circles of friends bring opportunities and challenges for recommender system (RS) to solve the cold start and sparsity problem of datasets. Some of the social factors have been used in RS, but have not been fully considered. In this paper, three social factors, personal interest, interpersonal interest similarity, and interpersonal influence, fuse into a unified personalized recommendation model based on probabilistic matrix factorization. The factor of personal interest can make the RS recommend items to meet users individualities, especially for experienced users. Moreover, for cold start users, the interpersonal interest similarity and interpersonal influence can enhance the intrinsic link among features in the latent space. We conduct a series of experiments on three rating datasets: Yelp, MovieLens, and Douban Movie. Experimental results show the proposed approach outperforms the existing RS approaches.

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 paper, 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 users 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 items 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.

Rating Prediction Based on Social Sentiment From Textual Reviews

In recent years, we have witnessed a flourish of review websites. It presents a great opportunity to share our viewpoints for various products we purchase. However, we face an information overloading problem. How to mine valuable information from reviews to understand a users preferences and make an accurate recommendation is crucial. Traditional recommender systems (RS) consider some factors, such as users purchase records, product category, and geographic location. In this work, we propose a sentiment-based rating prediction method (RPS) to improve prediction accuracy in recommender systems. Firstly, we propose a social user sentimental measurement approach and calculate each users sentiment on items/products. Secondly, we not only consider a users own sentimental attributes but also take interpersonal sentimental influence into consideration. Then, we consider product reputation, which can be inferred by the sentimental distributions of a user set that reflect customers comprehensive evaluation. At last, we fuse three factors-user sentiment similarity, interpersonal sentimental influence, and items reputation similarity-into our recommender system to make an accurate rating prediction. We conduct a performance evaluation of the three sentimental factors on a real-world dataset collected from Yelp. Our experimental results show the sentiment can well characterize user preferences, which helps to improve the recommendation performance.

Service Rating Prediction by Exploring Social Mobile Users’ Geographical Locations

Recently, advances in intelligent mobile device and positioning techniques have fundamentally enhanced social networks, which allows users to share their experiences, reviews, ratings, photos, check-ins, etc. The geographical information located by smart phone bridges the gap between physical and digital worlds. Location data functions as the connection between users physical behaviors and virtual social networks structured by the smart phone or web services. We refer to these social networks involving geographical information as location-based social networks (LBSNs). Such information brings opportunities and challenges for recommender systems to solve the cold start, sparsity problem of datasets and rating prediction. In this paper, we make full use of the mobile users’ location sensitive characteristics to carry out rating prediction. We mine: 1) the relevance between users ratings and user-item geographical location distances, called as user-item geographical connection, 2) the relevance between users’ rating differences and user-user geographical location distances, called as user-user geographical connection. It is discovered that humans’ rating behaviors are affected by geographical location significantly. Moreover, three factors: user-item geographical connection, user-user geographical connection, and interpersonal interest similarity, are fused into a unified rating prediction model. We conduct a series of experiments on a real social rating network dataset Yelp. Experimental results demonstrate that the proposed approach outperforms existing models.

Schedule a Rich Sentimental Travel via Sentimental POI Mining and Recommendation

Trip is an important part of peoples lives. We need to obtain interesting location information quickly and conveniently from the huge amount of information. The existing data source of geographic locations can be divided into two categories. The one is users information that we can utilize to deeply understand their trajectories, the other one is the characteristics and attributes of geographic locations. In this paper, we focus on sentimental attributes of location and propose a POI (Point-Of-Interest) Mining method. Firstly, we use SPM (Sentiment-based POI Mining) algorithm to mine the POIs (Points-Of-Interest) with obvious sentimental attributes, and then recommend the POIs to users by using SPR (Sentiment-based POI Recommendation) algorithm. We conduct a series of experiments in Sina Weibo dataset. The results show the effectiveness of our method.

Service Quality Evaluation by Exploring Social Users’ Contextual Information

Nowadays, with the boom of social media and e-commerce, more and more people prefer to share their consumption experiences and rate services on review sites. Much research has focused on personalized recommendation. However, quality of service also plays an important role in recommender systems, and it is the main concern of this paper. An overall rating that indicates the popular view usually represents the evaluation. There are some challenges when we do not have enough review information to extract public opinion. Take, for example, a movie for which one user rates a two star rating, and another rates a five star rating. In this case, it is difficult to conduct a quality evaluation fairly. However, it is possible to be improved with the help of big social users contextual information. In this paper, we propose a model to conduct service quality evaluation by improving overall rating of services using an empirical methodology. We use the concept of user ratings confidence, which denotes the trustworthiness of user ratings. First, entropy is utilized to calculate user ratings confidence. Second, we further explore spatial-temporal features and review sentimental features of user ratings to constrain their confidences. Last, we fuse them into a unified model to calculate an overall confidence, which is utilized to perform service quality evaluation. Extensive experiments implemented on Yelp and Douban Movie datasets demonstrate the effectiveness of our model.

Service Objective Evaluation via Exploring Social Users' Rating Behaviors

With the boom of e-commerce, it is a very popular trend for people to share their consumption experience and rate the items on a review site. The information they shared is valuable for new users to judge whether the items have high-quality services. Nowadays, many researchers focus on personalized recommendation and rating prediction. They miss the significance of service objective evaluation. Service objective evaluation is usually represented by star level, which is given by a large number of users. The more user ratings, the more objective evaluation is. But how does it work for new items? It is lack of objectivity if there are few users have rated to the item, such as there are just two ratings. In this paper, we propose a model to solve service objective evaluation by deep understanding social users. As we know, users tastes and habits are drifting over time. Thus, we focus on exploring user ratings confidence, which denotes the trustworthiness of user ratings in service objective evaluation. We utilize entropy to calculate user ratings confidence. In contrast, we mine the spatial and temporal features of user ratings to constrain confidence. We conduct a series of experiments based on Yelp datasets. Experimental results show the effectiveness of proposed model.

Personalized Recommendation by Exploring Social Users' Behaviors

With the popularity and rapid development of social network, more and more people enjoy sharing their experiences, such as reviews, ratings and moods. And there are great opportunities to solve the cold start and sparse data problem with the new factors of social network like interpersonal influence and interest based on circles of friends. Some algorithm models and social factors have been proposed in this domain, but have not been fully considered. In this paper, two social factors: interpersonal rating behaviors similarity and interpersonal interest similarity, are fused into a consolidated personalized recommendation model based on probabilistic matrix factorization. And the two factors can enhance the inner link between features in the latent space. We implement a series of experiments on Yelp dataset. And experimental results show the outperformance of proposed approach.

Location Recommendation for Enterprises by Multi-Source Urban Big Data Analysis

Effective location recommendation is an important problem in both research and industry. Much research has focused on personalized recommendation for users. However, there are more uses such as site selection for firms and factories. In this study, we try to solve site selection problem by recommending some locations satisfying special requirements. There are many factors affecting it, including functions of architecture, building cost, pollution discharge etc. We focus on the specific site selection of meteorological observation stations in this paper with leveraging the factors of functions of architecture and building cost from multi-source urban big data. We consider not only recommending the locations that can provide more accurate prediction and cover more areas, but also minimizing the cost of building new stations. We design an extensible two-stage framework for the station placing including prediction model and recommendation model. It is very convenient for executives to add more real-life factors into our approach. We have some empirical findings and evaluate the proposed approach using the real meteorological data of Shaanxi province, China. Experiment results show the better performance of our approach than existing commonly used methods.

Finding Optimal Meteorological Observation Locations by Multi-source Urban Big Data Analysis

In this paper, we try to solve site selection problem for building meteorological observation stations by recommending some locations. The functions of these stations are meteorological observation and prediction in regions without these. Thus in this paper two specific problems are solved. One is how to predict the meteorology in the regions without stations by using known meteorological data of other regions. The other is how to select the best locations to set up new observation stations. We design an extensible two-stage framework for the station placing including prediction model and selection model. It is very convenient for executives to add more real-life factors into our model. We consider not only selecting the locations that can provide the most accuracy predicted data but also how to minimize the cost of building new observation stations. We evaluate the proposed approach using the real meteorological data of Shaanxi province. Experiment results show the better performance of our model than existing commonly used methods.