An empirical study on effectiveness of temporal information as implicit ratings

Lee, Tong Queue; Park, Young; Park, Yong‐Tae

doi:10.1016/j.eswa.2007.11.047

Cited by 26 publications

(24 citation statements)

References 5 publications

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“…Similar observations have already been reported (see e.g. [14,15]) on other datasets where users' tastes evolve with time. Even though the reason might be different in our case (see Section 6), our results give additional evidence that the recency of user feedback is an important factor in CF.…”

Section: A Standard Approachsupporting

confidence: 74%

“…A second direction is the identification of domain dependent variables that describe interactions between users and items and which may influence the recommendation performance. For instance, purchase time can be used to take into account the recency of past customer purchases for the recommendation [7,15]. Another example is contextual variables such as the intent of a purchase (e.g.…”

Section: Introductionmentioning

confidence: 99%

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A case study in a recommender system based on purchase data

Pradel

Sean²,

Delporte

et al. 2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Collaborative filtering has been extensively studied in the context of ratings prediction. However, industrial recommender systems often aim at predicting a few items of immediate interest to the user, typically products that (s)he is likely to buy in the near future. In a collaborative filtering setting, the prediction may be based on the user's purchase history rather than rating information, which may be unreliable or unavailable. In this paper, we present an experimental evaluation of various collaborative filtering algorithms on a real-world dataset of purchase history from customers in a store of a French home improvement and building supplies chain. These experiments are part of the development of a prototype recommender system for salespeople in the store. We show how different settings for training and applying the models, as well as the introduction of domain knowledge may dramatically influence both the absolute and the relative performances of the different algorithms. To the best of our knowledge, the influence of these parameters on the quality of the predictions of recommender systems has rarely been reported in the literature.

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Section: A Standard Approachsupporting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

A case study in a recommender system based on purchase data

Pradel

Sean²,

Delporte

et al. 2011

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

View full text Add to dashboard Cite

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“…to consider the recorded time observing an object in a catalog). In this way, Lee et al [15] transform temporal information about purchasing items on pseudo-valuations. Celma [16] and Baltrunas & Amatriain [17] use information on how frequent each music track is for each user, in order to create explicit values on which it is possible to use CF algorithms.…”

Section: Related Work 21 Collaborative Recommendationmentioning

confidence: 99%

“…Some of these areas are, for example, the study of the variability in the users ratings [4], the users coverage of the dataset [5], information provided by external users or experts [6], the temporal dimension [7], [8], [9] and the use of spatialtemporal information [3]. Frequently there exist limited data to perform the mining process.…”

Section: Related Work 21 Collaborative Recommendationmentioning

confidence: 99%

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Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

Díez

Chavarriaga

Campos

et al. 2010

Proceedings of the Workshop on Context-Aware Movie Recommendation

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In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.

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Social media marketing through time‐aware collaborative filtering

Maio

Fenza

Gallo

et al. 2017

Concurrency and Computation

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SummarySocial media is assuming a crucial role in purchasing decisions, and most companies are using social media for marketing. Making sense of the unstructured information content shared by users along the time is an emerging challenge. We advise that the dynamic nature with which trends and user's interests evolve along the timeline requires the revising of well-assessed methods to address, for instance, recommendation provisioning, information retrieval, and so on. ie, Twitter. 6 In particular, we advise that time awareness is crucial to more effectively estimate user's interests, and thus, to better address social media marketing improving "vanity metrics" of likes and shares that in social marketing remain the most commonly used measures for evaluating success on social media attempting to increase traction, for instance, posting the right message at the right time.Problem. Let us consider a timestamped tweet stream = {tw 1 , … , tw n } in the time window starting at time t s and ending at time t e , and let us suppose topics corresponding to the content of each

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An empirical study on effectiveness of temporal information as implicit ratings

Cited by 26 publications

References 5 publications

A case study in a recommender system based on purchase data

A case study in a recommender system based on purchase data

Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

Social media marketing through time‐aware collaborative filtering

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