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Konstan, Joseph A.; Riedl, Joachim

doi:10.1109/mspec.2012.6309257

Cited by 26 publications

(13 citation statements)

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“…Recommender systems, one of the most important computer-based intelligent approaches to find out the most appropriate services or goods from a large amount of products, are proved to be important tools that overcome the information overload by sifting through the large set of data and recommending information relevant to the user [6][7][8][9][10]. Typically, in e-commerce environment a recommender system analyzes trading data between consumer and sellers and items to find associations among them, and the items bought by similar users are presented as recommendations.…”

confidence: 99%

“…The acquiescent assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue T than to have the opinion on T of a person chosen randomly. For example, a collaborative filtering recommendation system for laptop preferences could make forecasts about which laptop a user should like given a partial list of that user's preferences (likes or dislikes) [10]. However there are two main inherent weaknesses in the collaborative filtering based recommendation systems [8]: (1) It is a challenge to find similar user, because the probability of two random users have rated any items in common is very small, and hence they are hardly comparable.…”

confidence: 99%

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Study on Directed Trust Graph Based Recommendation for E-commerce System

Zhong

Zhang

Wang

et al. 2014

INT J COMPUT COMMUN

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Automated recommender systems have played a more and more important role in marketing and ever increasingly booming e-commerce systems. They provide useful predictions personalized recommendations according to customers characteristics and a variety of large and complex product offerings. In many of these recommendation technologies Collaborative Filtering (CF) has proven to be one of the most successful recommendation method, which has been widely used in many e-commerce systems. The success of CF recommendation depends mainly on locating similar neighbors to get recommendation items. However, many scholars have found that the process of finding similar neighbors often fail, due to some inherent weaknesses of CF based recommendation. In view of this, we propose a trust feedback recommendation algorithm based on directed trust graph (DTG), which is able to propagate trust relationship. In our approach, there is no need to compute similarity between users, but utilize the trust relation between them to conduct prediction calculation. Based on the analysis of human trust perception, we incorporate the process into our recommendation algorithm. Experimental evaluation on real life Epinions datasets shows that the effectiveness and practicability of our approach.

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confidence: 99%

Study on Directed Trust Graph Based Recommendation for E-commerce System

Zhong

Zhang

Wang

et al. 2014

INT J COMPUT COMMUN

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“…Collaborative Filtering (CF) has been widely used by major commercial applications such as Amazon, Movielens, or Netflix [24,27,1]. These methods leverage users rating history and predict the rating of a target item and a source user by looking at the ratings on the target item given by similar users, user-based approaches [17], or at what ratings items similar to the target one have received from the source user, item-based approaches [38].…”

Section: Related Workmentioning

confidence: 99%

“…Amazon or Netflix, to provide users with books or movies that match their interest. Accurate recommendations generate returns of investments up to 30% due to increased sales [24]. Many such systems rely on collaborative filtering (CF) approaches that recommend items based on user rating history.…”

Section: Introductionmentioning

confidence: 99%

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations

Olteanu

Kermarrec

Aberer

2014

Web Information Systems Engineering – WISE 2014

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Abstract. The advent of online social networks created new prediction opportunities for recommender systems: instead of relying on past rating history through the use of collaborative filtering (CF), they can leverage the social relations among users as a predictor of user tastes similarity. Alas, little effort has been put into understanding when and why (e.g., for which users and what items) the social affinity (i.e., how well connected users are in the social network) is a better predictor of user preferences than the interest affinity among them as algorithmically determined by CF, and how to better evaluate recommendations depending on, for instance, what type of users a recommendation application targets. This overlook is explained in part by the lack of a systematic collection of datasets including both the explicit social network among users and the collaborative annotated items. In this paper, we conduct an extensive empirical analysis on six real-world publicly available datasets, which dissects the impact of user and item attributes, such as the density of social ties or item rating patterns, on the performance of recommendation strategies relying on either the social ties or past rating similarity. Our findings represent practical guidelines that can assist in future deployments and mixing schemes.

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“…In fact, a study found an increase of 50% on song sales when a group of users where exposed to recommendations [26]. However, due to their sophistication, such engines are highly demanding on computing resources [41]. The developer can specify that the execution of the recommender engine is optional.…”

Section: Introductionmentioning

confidence: 99%

Brownout: building more robust cloud applications

Klein

Maggio

Årzén

et al. 2014

Proceedings of the 36th International Conference on Software Engineering

117

134

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Self-adaptation is a first class concern for cloud applications, which should be able to withstand diverse runtime changes. Variations are simultaneously happening both at the cloud infrastructure levelfor example hardware failures -and at the user workload levelflash crowds. However, robustly withstanding extreme variability, requires costly hardware over-provisioning.In this paper, we introduce a self-adaptation programming paradigm called brownout. Using this paradigm, applications can be designed to robustly withstand unpredictable runtime variations, without over-provisioning. The paradigm is based on optional code that can be dynamically deactivated through decisions based on control theory.We modified two popular web application prototypes -RUBiS and RUBBoS -with less than 170 lines of code, to make them brownout-compliant. Experiments show that brownout selfadaptation dramatically improves the ability to withstand flashcrowds and hardware failures.

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Cited by 26 publications

References 0 publications

Study on Directed Trust Graph Based Recommendation for E-commerce System

Study on Directed Trust Graph Based Recommendation for E-commerce System

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations

Brownout: building more robust cloud applications

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