Improving new user recommendations with rule-based induction on cold user data

Nguyen, An-Te; Denos, Nathalie; Berrut, Catherine

doi:10.1145/1297231.1297251

Cited by 45 publications

(31 citation statements)

References 10 publications

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“…Including ratings before this time aims to remove the system-wide cold-start problem that recommender systems face, i.e. a state where predictions can not be made as there are little to no historical ratings available [7]. In this work, we set = 500 days from the first rating in the dataset, and our final subsets have about 60, 000 users: setting the value as we did is equivalent to bootstrapping a recommender system with 10, 000 users.…”

Section: Temporal Experimentsmentioning

confidence: 99%

Temporal collaborative filtering with adaptive neighbourhoods

Lathia

Hailes

Capra

2009

Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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Recommender Systems, based on collaborative filtering (CF), aim to accurately predict user tastes, by minimising the mean error achieved on hidden test sets of user ratings, after learning from a training set. However, deployed recommender systems do not operate on, and should not be optimised to predict, a static set of user ratings because the underlying dataset is continuously growing and changing. The aim of a recommender system is therefore to iteratively predict users' preferences over a dynamic dataset, and system administrators are confronted with the problem of having to continuously tune the parameters calibrating their CF algorithm for best performance.In this work, we first formalise CF as a time-dependent, iterative prediction problem. We then perform a temporal analysis of the Netflix dataset, and evaluate the temporal performance of a baseline model and the k-Nearest Neighbour algorithm. We show that, due to the dynamic nature of the data, certain prediction methods that improve prediction accuracy on the Netflix probe set do not show similar improvements over a set of iterative train-test experiments with growing data. We then address the problem of parameter selection and update, and propose a method to automatically assign and update per-user neighbourhood sizes that (on the temporal scale) outperforms setting global parameters.

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Section: Temporal Experimentsmentioning

confidence: 99%

Temporal collaborative filtering with adaptive neighbourhoods

Lathia

Hailes

Capra

2009

Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval

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“…Moreover, rule-based induction [5] on cold user data has been also used to improve new user recommendation. It combines with various-community spaces which relate to cold user data (age, occupation, location, etc.).…”

Section: Related Workmentioning

confidence: 99%

“…Then, the set B' can be obtained combined with the cliques and the set B'' further can be formed according to preferential attachment characteristic. Finally, the neighbor set of new user is formed according to the equation (5).…”

Section: Datasetmentioning

confidence: 99%

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On Alleviation of New User Problem in Collaborative Filtering using SNA Theory

Yang¹,

Zhang²,

Wang³

2013

IJUNESST

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“…This paper also focuses on binary Web usage data, however it differentiates between different types of usage information. Therefore, the approach is also related to the α-community spaces model, where different user features termed α i are employed for determining similar users [15]. Nguyen et al determine clusters of similar users according to feature α i and use a rule-based induction approach to derive recommendations from the corresponding cluster.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback

Zanker

Jessenitschnig

2009

2009 IEEE Conference on Commerce and Enterprise Computing

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Collaborative filtering (CF) is currently the most popular technique used in commercial recommender systems. Algorithms of this type derive personalized product propositions for customers by exploiting statistics derived from vast amounts of transaction data. Traditionally, basic CF algorithms have exploited a single category of ratings despite the fact that on many platforms a variety of different forms of user feedback are available for personalization and recommendation. In this paper we explore a collaborative feature-combination algorithm that concurrently exploits multiple aspects of the user model like clickstream data, sales transactions and explicit user requirements to overcome some known shortcomings of CF like the cold-start problem for new users. We validate our contribution by evaluating it against the standard user-to-user CF algorithm using a dataset from a commercial Web shop. Evaluation results indicate considerable improvements in terms of user coverage and accuracy.

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Improving new user recommendations with rule-based induction on cold user data

Cited by 45 publications

References 10 publications

Temporal collaborative filtering with adaptive neighbourhoods

Temporal collaborative filtering with adaptive neighbourhoods

On Alleviation of New User Problem in Collaborative Filtering using SNA Theory

Collaborative Feature-Combination Recommender Exploiting Explicit and Implicit User Feedback

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