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Personalized recommendation with corrected similarity  [PDF]
Xuzhen Zhu,Hui Tian,Shimin Cai
Computer Science , 2014, DOI: 10.1088/1742-5468/2014/07/P07004
Abstract: Personalized recommendation attracts a surge of interdisciplinary researches. Especially, similarity based methods in applications of real recommendation systems achieve great success. However, the computations of similarities are overestimated or underestimated outstandingly due to the defective strategy of unidirectional similarity estimation. In this paper, we solve this drawback by leveraging mutual correction of forward and backward similarity estimations, and propose a new personalized recommendation index, i.e., corrected similarity based inference (CSI). Through extensive experiments on four benchmark datasets, the results show a greater improvement of CSI in comparison with these mainstream baselines. And the detailed analysis is presented to unveil and understand the origin of such difference between CSI and mainstream indices.
Research on the Personalized Recommendation Algorithm for Hairdressers  [PDF]
Zhixin Liang, Fengying Wang
Open Journal of Modelling and Simulation (OJMSi) , 2016, DOI: 10.4236/ojmsi.2016.43009
Abstract: In order to retain customers, hairdressers usually persuade customers to be their members by offering membership card. This paper studies how to set up their recommendation system in the hairdressers. According to the membership information and consumer behavior, the hairdresser provides personalized recommendation to different members and lets customers experience personalized choices. A recommendation algorithm based on customer ratings and a customer classification method based on Logistic Regression Model are discussed in this paper. The former is used to recommend hair style and color to a customer. The latter is used to determine whether to recommend some maintenance programs to a customer or not.
Empirical Study and Model of User Acceptance for Personalized Recommendation  [cached]
Zheng Hua
Research Journal of Applied Sciences, Engineering and Technology , 2013,
Abstract: Personalized recommendation technology plays an important role in the current e-commerce system, but the user willingness to accept the personalized recommendation and its influencing factors need to be study. In this study, the Theory of Reasoned Action (TRA) and Technology Acceptance Model (TAM) are used to construct a user acceptance model of personalized recommendation which tested by the empirical method. The results show that perceived usefulness, perceived ease of use, subjective rules and trust tend had an impact on personalized recommendation.
Effect of user tastes on personalized recommendation  [PDF]
Jian-Guo Liu,Tao Zhou,Qiang Guo,Bing-Hong Wang,Yi-Cheng Zhang
Physics , 2009, DOI: 10.1142/S0129183109014825
Abstract: In this paper, based on a weighted projection of the user-object bipartite network, we study the effects of user tastes on the mass-diffusion-based personalized recommendation algorithm, where a user's tastes or interests are defined by the average degree of the objects he has collected. We argue that the initial recommendation power located on the objects should be determined by both of their degree and the users' tastes. By introducing a tunable parameter, the user taste effects on the configuration of initial recommendation power distribution are investigated. The numerical results indicate that the presented algorithm could improve the accuracy, measured by the average ranking score, more importantly, we find that when the data is sparse, the algorithm should give more recommendation power to the objects whose degrees are close to the users' tastes, while when the data becomes dense, it should assign more power on the objects whose degrees are significantly different from user's tastes.
A Personalized Recommendation Algorithm Based on Associative Sets  [PDF]
Guorui JIANG, Hai QING, Tiyun HUANG
Journal of Service Science and Management (JSSM) , 2009, DOI: 10.4236/jssm.2009.24048
Abstract: During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users’ real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent item sets to filter out noise data, and makes recommendations according to users’ real preferences, so as to enhance the accuracy of recommending results. Test results have proved the superiority of this algorithm.
Personalized recommendation against crowd's popular selection  [PDF]
Xuzhen Zhu,Hui Tian,Haifeng Liu,Shimin Cai
Computer Science , 2014,
Abstract: The problem of personalized recommendation in an ocean of data attracts more and more attention recently. Most traditional researches ignore the popularity of the recommended object, which resulting in low personality and accuracy. In this Letter, we proposed a personalized recommendation method based on weighted object network, punishing the recommended object that is the crowd's popular selection, namely, Anti-popularity index(AP), which can give enhanced personality, accuracy and diversity in contrast to mainstream baselines with a low computational complexity.
Effective Personalized Recommendation in Collaborative Tagging Systems  [PDF]
Zi-Ke Zhang,Tao Zhou
Computer Science , 2009,
Abstract: Recently, collaborative tagging systems have attracted more and more attention and have been widely applied in web systems. Tags provide highly abstracted information about personal preferences and item content, and are therefore potential to help in improving better personalized recommendations. In this paper, we propose a tag-based recommendation algorithm considering the personal vocabulary and evaluate it in a real-world dataset: Del.icio.us. Experimental results demonstrate that the usage of tag information can significantly improve the accuracy of personalized recommendations.
Personalized News Recommendation with Context Trees  [PDF]
Florent Garcin,Christos Dimitrakakis,Boi Faltings
Computer Science , 2013, DOI: 10.1145/2507157.2507166
Abstract: The profusion of online news articles makes it difficult to find interesting articles, a problem that can be assuaged by using a recommender system to bring the most relevant news stories to readers. However, news recommendation is challenging because the most relevant articles are often new content seen by few users. In addition, they are subject to trends and preference changes over time, and in many cases we do not have sufficient information to profile the reader. In this paper, we introduce a class of news recommendation systems based on context trees. They can provide high-quality news recommendation to anonymous visitors based on present browsing behaviour. We show that context-tree recommender systems provide good prediction accuracy and recommendation novelty, and they are sufficiently flexible to capture the unique properties of news articles.
Personalized Social Recommendations - Accurate or Private?  [PDF]
Ashwin Machanavajjhala,Aleksandra Korolova,Atish Das Sarma
Computer Science , 2011,
Abstract: With the recent surge of social networks like Facebook, new forms of recommendations have become possible - personalized recommendations of ads, content, and even new friend and product connections based on one's social interactions. Since recommendations may use sensitive social information, it is speculated that these recommendations are associated with privacy risks. The main contribution of this work is in formalizing these expected trade-offs between the accuracy and privacy of personalized social recommendations. In this paper, we study whether "social recommendations", or recommendations that are solely based on a user's social network, can be made without disclosing sensitive links in the social graph. More precisely, we quantify the loss in utility when existing recommendation algorithms are modified to satisfy a strong notion of privacy, called differential privacy. We prove lower bounds on the minimum loss in utility for any recommendation algorithm that is differentially private. We adapt two privacy preserving algorithms from the differential privacy literature to the problem of social recommendations, and analyze their performance in comparison to the lower bounds, both analytically and experimentally. We show that good private social recommendations are feasible only for a small subset of the users in the social network or for a lenient setting of privacy parameters.
A novel similarity index for better personalized recommendation  [PDF]
Ling-Jiao Chen,Zi-Ke Zhang,Jin-Hu Liu,Jian Gao,Tao Zhou
Computer Science , 2015,
Abstract: Recommender systems benefit us in tackling the problem of information overload by predicting our potential choices among diverse niche objects. So far, a variety of personalized recommendation algorithms have been proposed and most of them are based on similarities, such as collaborative filtering and mass diffusion. Here, we propose a novel similarity index named CosRA, which combines advantages of both the cosine index and the resource-allocation (RA) index. By applying the CosRA index to real recommender systems including MovieLens, Netflix and RYM, we show that the CosRA-based method has better performance in accuracy, diversity and novelty than the state-of-the-art methods. Moreover, the CosRA index is free of parameters, which is a significant advantage in real applications. Further experiments show that the introduction of two turnable parameters cannot remarkably improve the overall performance of CosRA.
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