Precision-oriented evaluation of recommender systems

Bellogín, Alejandro; Castells, Pablo; Cantador, Iván

doi:10.1145/2043932.2043996

Cited by 156 publications

(126 citation statements)

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“…For instance, MAE is used in (Sarwar et al 2001) whereas hit-rate is used in (Deshpande and Karypis 2004). In prior work (Bellogín et al 2011a), we already observed an item-based method with low (i.e. good) RMSE and a close to zero precision, evidencing that a well performing method in terms of error metrics does not necessarily have a good performance in terms of precision.…”

Section: Item Ranking Taskmentioning

confidence: 92%

Bridging memory-based collaborative filtering and text retrieval

2012

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription Abstract When speaking of information retrieval, we often mean text retrieval. But there exist many other forms of information retrieval applications. A typical example is collaborative filtering that suggests interesting items to a user by taking into account other users' preferences or tastes. Due to the uniqueness of the problem, it has been modeled and studied differently in the past, mainly drawing from the preference prediction and machine learning view point. A few attempts have yet been made to bring back collaborative filtering to information (text) retrieval modeling and subsequently new interesting collaborative filtering techniques have been thus derived.In this paper, we show that from the algorithmic view point, there is an even closer relationship between collaborative filtering and text retrieval. Specifically, major collaborative filtering algorithms, such as the memory-based, essentially calculate the dot product between the user vector (as the query vector in text retrieval) and the item rating vector (as the document vector in text retrieval). Thus, if we properly structure user preference data and employ the target user's ratings as query input, major text retrieval algorithms and systems can be directly used without any modification. In this regard, we propose a unified formulation under a common notational framework for memory-based collaborative filtering, and a technique to use any text retrieval weighting function with collaborative filtering preference data. Besides confirming the rationale of the framework, our preliminary experimental results have also demonstrated the effectiveness of the approach in using text retrieval models and systems to perform item ranking tasks in collaborative filtering.

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Section: Item Ranking Taskmentioning

confidence: 92%

Bridging memory-based collaborative filtering and text retrieval

2012

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“…Besides, it is not possible to apply metrics such as MAE and RMSE to our approaches, since the proposed methods rank items, but do not generate rating predictions. The methodology used in the evaluation corresponds to the TestItems approach described in (Bellogín, Castells, and Cantador, 2011a), where, for each user, a ranking is generated by predicting a score for every item in the test set, only ignoring those items already rated by the user (i.e., in training). We also tested alternative methodologies, such as the one proposed by (Koren, 2008) where a ranking is generated for each item in the test set based on N additional not-relevant items.…”

Section: Evaluation Methodologymentioning

confidence: 99%

Relevance-based language modelling for recommender systems

Parapar

Bellogín

Castells

et al. 2013

Information Processing & Management

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Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: AbstractRelevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical Language Modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the pseudo relevance feedback task. On the other hand, the field of Recommender Systems is a fertile research area where users are provided with personalised recommendations in several applications. In this paper, we propose an adaptation of the Relevance Modelling framework to effectively suggest recommendations to a user. We also propose a probabilistic clustering technique to perform the neighbour selection process as a way to achieve a better approximation of the set of relevant items in the pseudo relevance feedback process. These techniques, although well known in the Information Retrieval field, have not been applied yet to recommender systems, and, as the empirical evaluation results show, both proposals outperform individually several baseline methods. Furthermore, by combining both approaches even larger effectiveness improvements are achieved.

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“…Based on the obtained predicted ratings, in case of RSVD, and obtained scores, in proposed model, top 5 and top 10 items are presented to the user. The predicted rating and predicted scores respectively in descending order are presented to every user and then accuracy measures such as precision are obtained [24]. figure 2 show the precision of the proposed cosine based latent factor model and RSVD on ml-100k dataset and FilmTrust dataset respectively.…”

Section: Evaluating the Performance Of Modelsmentioning

confidence: 99%

Cosine Based Latent Factor Model for Precision Oriented Recommendation

Kumar¹,

Bala²,

Srivastava³

2016

ijacsa

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Abstract-Recommender systems suggest a list of interesting items to users based on their prior purchase or browsing behaviour on e-commerce platforms. The continuing research in recommender systems have primarily focused on developing algorithms for rating prediction task. However, most e-commerce platforms provide 'top-k' list of interesting items for every user. In line with this idea, the paper proposes a novel machine learning algorithm to predict a list of 'top-k' items by optimizing the latent factors of users and items with the mapped scores from ratings. The basic idea is to learn latent factors based on the cosine similarity between the users and items latent features which is then used to predict the scores for unseen items for every user. Comprehensive empirical evaluations on publicly available benchmark datasets reveal that the proposed model outperforms the state-of-the-art algorithms in recommending good items to a user.

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Precision-oriented evaluation of recommender systems

Cited by 156 publications

References 10 publications

Bridging memory-based collaborative filtering and text retrieval

Bridging memory-based collaborative filtering and text retrieval

Relevance-based language modelling for recommender systems

Cosine Based Latent Factor Model for Precision Oriented Recommendation

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