A Rule Based Approach to Group Recommender Systems

Padmanabhan, Vineet; Seemala, Siva Krishna; Bhukya, Wilson Naik

doi:10.1007/978-3-642-25725-4_3

Cited by 8 publications

(8 citation statements)

References 13 publications

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“…Vineet et.al., (Padmanabhan et al, 2011) proposed a movie group recommender System based on Decision List Rule Learner (Rivest, 1987;Cohen, 1995;Quinlan, 1996) and social choice theory strategies (Masthoff, 2003) (here we refer their approach as VSW Method). They used a Data set of 150 movies where each is a collection of 12 attribute-value pair.…”

Section: Vsw Methodsmentioning

confidence: 99%

“…So either E or F is the recommended program. (Padmanabhan et al, 2011) suggested that a single strategy alone would not be sufficient to get the most accurate result as far as group recommendation is concerned. To address this problem a combined strategy was put forward that considers three factors: (1) Least group member happy (like least misery strategy) (2) Most group member happy (like most pleasure strategy) and (3) Total group happy (like Utilitarian strategy) and named the strategy as RTL (Repeat Total plus Least group happiness strategy).…”

Section: Vsw Methodsmentioning

confidence: 99%

“…(2) It is difficult to specify the additional information which may be required from the user to determine the exact combination/aggregation strategy and (3) Lot of time will be required even if opinion from domain experts are sought to guide the combination process. In this paper we follow our previous work on group recommender system (Padmanabhan et al, 2011) wherein the group recommender problem is defined as:…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Group Recommender Systems - Some Experimental Results

Padmanabhan¹,

Kiran²,

Sattar³

2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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Recommender Systems (RS) are software applications which aim to support users in their decision making while interacting with large information spaces. Most recommender systems are designed for recommending items to individuals. In this paper we provide experimental results related to developing a content-based group recommender system. To this end we make two important contributions. (1) Implementation of a group recommender system based on decision-lists as proposed recently in (Padmanabhan et al., 2011) using MovieLens dataset which is a relatively huge data-set (100,000 ratings from 943 users on 1682 movies) as compared to the data-set size of 150 used in (Padmanabhan et al., 2011) (2) We use seven variants of decision-tree measures and built an empirical comparison table to check for precision rate in group recommendation based on different social-choice theory strategies.

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Section: Vsw Methodsmentioning

confidence: 99%

Section: Vsw Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Group Recommender Systems - Some Experimental Results

Padmanabhan¹,

Kiran²,

Sattar³

2013

Proceedings of the 5th International Conference on Agents and Artificial Intelligence

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“…Webscope (R4), which are two of the most widely adopted datasets in the recommender systems literature. In the literature it has been highlighted that no dataset that contains a partitioning of the users into groups exists [42,46]. The existing group recommender system either add constraints on a dataset like the ones we adopted (the vast majority), obtain datasets from social networks that involve the concept of group like Flickr [50] and Facebook [3], or perform use-case studies to perform the evaluation [37].…”

Section: Strategymentioning

confidence: 99%

ART: group recommendation approaches for automatically detected groups

Boratto

Carta

2015

Int. J. Mach. Learn. & Cyber.

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Group recommender systems provide suggestions when more than a person is involved in the recommendation process. A particular context in which group recommendation is useful is when the number of recommendation lists that can be generated is limited (i.e., it is not possible to suggest a list of items to each user). In such a case, grouping users and producing recommendations to groups becomes necessary. None of the approaches in the literature is able to automatically group the users in order to overcome the previously presented limitation. This paper presents a set of group recommender systems that automatically detect groups of users by clustering them, in order to respect a constraint on the maximum number of recommendation lists that can be produced. The proposed systems have been largely evaluated on two real-world datasets and compared with hundreds of experiments and statistical tests, in order to validate the results. Moreover, we introduce a set of best practices that help in the development of group recommender systems in this context.

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“…Collaborative filtering [2] is one of the commonly used methods for developing personal as well as group recommender systems [1,8,5,3,4]. Two main techniques used in collaborative filtering are called Memory-based and Model-based.…”

Section: Introductionmentioning

confidence: 99%

Precedence Mining in Group Recommender Systems

Kagita

Padmanabhan

Pujari

2013

Lecture Notes in Computer Science

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Abstract. We extend the Precedence mining model for personal recommendation as outlined in Parameswaran et.al.,[6] in three different ways. Firstly, we show how precedence mining model can be used for recommending items of interest to a group of users (group recommendation) and compare and contrast our model with traditional group recommendation models like collaborative and Hybrid. Secondly, we extend the precedence mining model to incorporate ratings for items and experimental results show that the goodness of recommendation is improved. The third extension is related to the issue of new items being ignored which is a fundamental problem plaguing collaborative and precedence mining algorithms. When recommendations are based on other users interests (like in Collaborative recommender systems) the possibility of not recommending a new item which has not been consumed by many of the users is high though the new item may be of interest to the target user. We outline two models, Vector precedence-mining and Hybrid precedence-mining that addresses this issue.

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A Rule Based Approach to Group Recommender Systems

Cited by 8 publications

References 13 publications

Group Recommender Systems - Some Experimental Results

Group Recommender Systems - Some Experimental Results

ART: group recommendation approaches for automatically detected groups

Precedence Mining in Group Recommender Systems

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