Novel Recommendation Based on Personal Popularity Tendency

Oh, Jinoh; Park, Sun; Yu, Hwanjo; Song, Min; Park, Seung-Taek

doi:10.1109/icdm.2011.110

Cited by 70 publications

(37 citation statements)

References 16 publications

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“…Where represents the prediction score for a user u and each item i in a list of recommendations, is the number of users and the number of users who liked item i. Jinoh, Oh proposes an efficient novel-recommendation method called Personal Popularity Tendency Matching (PPTM) which recommends novel items by considering an individual's Personal Popularity Tendency (or PPT) [31].…”

Section: Related Workmentioning

confidence: 99%

The Definition of Novelty in Recommendation System

Zhang¹

2013

JESTR

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With the development of information technology and application of the Internet, People gradually entered the time of information overload from information scarcity. User satisfaction with recommender systems is related not only to how accurately the system recommends but also to how much it supports the user's decision making. Novelty is one of the important metrics of customer satisfaction. There is an increasing realization in the Recommender Systems (RS) field that novelty is fundamental qualities of recommendation effectiveness and added-value. This paper combed research results about definition and algorithm of novel recommendation, and starting from the meaning of "novel", defined novelty of item in recommendation system. Experiment proved using the definition of novelty to recommend can effectively recognize the item that the user is familiar with and ensure certain accuracy.

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Section: Related Workmentioning

confidence: 99%

The Definition of Novelty in Recommendation System

Zhang¹

2013

JESTR

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“…To solve these problems, some studies have attempted to develop several metrics to quantify the diversity of recommendation results [17,20,21] and further to propose new recommendation models for improving the diversity. Among these studies, some of these models are composed of a relevance-oriented part and a diversity-oriented part [21], while some other models take into account relevance and diversity in a unified framework [20,22]. The two-part hybrid modes can incorporate traditional recommendation algorithms and leverage their advantages, while the unified diversification frameworks are more compact and principled in terms of formulation.…”

Section: Introductionmentioning

confidence: 99%

A framework for diversifying recommendation lists by user interest expansion

Zhang

Zheng

Zeng

2016

Knowledge-Based Systems

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Recommender systems have been widely used to discover users’ preferences and recommend interesting items to users during this age of information load. Researchers in the field of recommender systems have realized that the quality of a top-N recommendation list involves not only relevance but also diversity. Most traditional recommendation algorithms are difficult to generate a diverse item list that can cover most of his/her interests for each user, since they mainly focus on predicting accurate items similar to the dominant interests of users. Additionally, they seldom exploit semantic information such as item tags and users’ interest labels to improve recommendation diversity. In this paper, we propose a novel recommendation framework which mainly adopts an expansion strategy of user interests based on social tagging information. The framework enhances the diversity of users’ preferences by expanding the sizes and categories of the original user-item interaction records, and then adopts traditional recommendation models to generate recommendation lists. Empirical evaluations on three real-world data sets show that our method can effectively improve the accuracy and diversity of item recommendation.

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“…The authors formulated the trade-off between novelty and matching quality as a binary optimization problem and used an explicit control parameter to allow the tuning of this trade-off. Oh et al [28] proposed an efficient novel-recommendation method which can help to diversify recommendations by reasonably penalizing popular items while improving the recommendation accuracy. Zhou et al [48] presented an algorithm specifically to address the challenge of novelty and diversity, and showed it can be used to resolve the novelty-accuracy dilemma when combined in an elegant hybrid with an accuracyfocused algorithm.…”

Section: Novelty In Recommendationmentioning

confidence: 99%

“…This algorithm proposed in [28] gives an effective novel item recommendation by reasonably penalizing popular items while improving the recommendation accuracy, which is a state-ofthe-art method in recommending novel items.…”

Section: Experiments For Novel Restaurant Recommendationmentioning

confidence: 99%

A Novelty-Seeking based Dining Recommender System

Zhang

Zheng

Yuan

et al. 2015

Proceedings of the 24th International Conference on World Wide Web

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The rapid growth of location-based services provide the potential to understand people's mobility pattern at an unprecedented level, which can also enable food-service industry to accurately predict consumer's dining behavior. In this paper, by leveraging users' historical dining pattern, socio-demographic characteristics and restaurants' attributes, we aim at generating the top-K restaurants for a user's next dining. Compared to previous studies in location prediction which mainly focus on regular mobility patterns, we present a novelty-seeking based dining recommender system, termed NDRS, in consideration of both exploration and exploitation. First, we apply a Conditional Random Field (CRF) with additional constraints to infer users' novelty-seeking statuses by considering both spatial-temporal-historical features and users' socio-demographic characteristics. On the one hand, when a user is predicted to be novelty-seeking, by incorporating the influence of restaurants' contextual factors such as price and service quality, we propose a context-aware collaborative filtering method to recommend restaurants she has never visited before. On the other hand, when a user is predicted to be not novelty-seeking, we then present a Hidden Markov Model (HMM) considering the temporal regularity to recommend the previously visited restaurants. To evaluate the performance of each component as well as the whole system, we conduct extensive experiments, with a large dataset we have collected covering the concerned dining related check-ins, users' demographics, and restaurants' attributes. The results reveal that our system is effective for dining recommendation.

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Novel Recommendation Based on Personal Popularity Tendency

Cited by 70 publications

References 16 publications

The Definition of Novelty in Recommendation System

The Definition of Novelty in Recommendation System

A framework for diversifying recommendation lists by user interest expansion

A Novelty-Seeking based Dining Recommender System

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