Methods and metrics for cold-start recommendations

Schein, Andrew I.; Popescul, Alexandrin; Ungar, Lyle H.; Pennock, David M.

doi:10.1145/564418.564421

Cited by 413 publications

(460 citation statements)

References 5 publications

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“…A typical problem in traditional CF recommendation is the ''cold start'' problem. It is hard to generate recommendations for new items because there is not enough preference data about the new items to make reliable correlations with other items [37].…”

Section: Discussionmentioning

confidence: 99%

Utilizing learning process to improve recommender system for group learning support

Wan

Okamoto

2009

Neural Comput & Applic

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With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains, it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained mainly by two mechanisms: the learners' learning processes and the analysis of social interaction. Therefore, this study proposes a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners' learning activities and learning processes so that we can correctly estimate the rating which also include learners' social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified recommendation.

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Section: Discussionmentioning

confidence: 99%

Utilizing learning process to improve recommender system for group learning support

Wan

Okamoto

2009

Neural Comput & Applic

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“…Recommendation methods are usually classified into three categories [2]: Content-based recommendations [3,13,32,36,37], CF [17,23,[38][39][40] and Hybrid approaches [11,31,41,48]. Content-based methods make recommendations based on the similarity between the user and the item's content profile.…”

Section: Related Workmentioning

confidence: 99%

“…Traditional CF techniques have achieved successful results in rating prediction problems, such as Netflix's movie recommendation. However, it suffers from the well-known cold-start problem [41], where few ratings can be obtained when a new item or user enters to the system.…”

Section: Related Workmentioning

confidence: 99%

“…Burke et al [10] employ mixture models which build the recommendation based on a linear combination of the Content-based prediction and collaborative prediction. Schein et al [41] propose to unify CF and Content-based evidence by probabilistic mixture of aspects. Recently, more and more works focus on social media recommendation [42,50,51].…”

Section: Related Workmentioning

confidence: 99%

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Enriching one-class collaborative filtering with content information from social media

et al. 2014

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In recent years, recommender systems have become popular to handle the information overload problem of social media websites. The most widely used Collaborative Filtering methods make recommendations by mining users' rating history. However, users' behaviors in social media are usually implicit, where no ratings are available. This is a One-Class Collaborative Filtering (OCCF) problem with only positive examples. How to distinguish the negative examples from missing data is important for OCCF. Existing OCCF methods tackle this by the statistical properties of users' historical behavior; however, they ignored the rich content information in social media websites, which provide additional evidence for profiling users and items. In this paper, we propose to improve OCCF accuracy by exploiting the social media content information to find the potential negative examples from the missing user-item pairs. Specifically, we get a content topic feature for each user and item by probabilistic topic modeling and embed them into the Matrix Factorization model. Extensive experiments show that our algorithm can achieve better performance than the state-ofart methods.

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“…In a manner similar to the sparsity problem, the number of ratings on the part of a particular user is important to making recommendations. CF-based recommendations initially have poor performance, because they are based on users who rate only a few items and on items with very few or no ratings; this is called the "cold start problem" [26,28]. To address this issue, many e-businesses exploit other recommendation strategies, such as user clustering based on demographic information, and popularity-based recommendations [28].…”

Section: Parameter Selection and Collaborative Filteringmentioning

confidence: 99%

Understanding collaborative filtering parameters for personalized recommendations in e-commerce

Lee

Kim

Park

2007

Electron Commerce Res

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Collaborative Filtering (CF) is a popular method for personalizing product recommendations for e-Commerce and customer relationship management (CRM). CF utilizes the explicit or implicit product evaluation ratings of customers to develop personalized recommendations. However, there has been no in-depth investigation of the parameters of CF in relation to the number of ratings on the part of an individual customer and the total number of ratings for an item.We empirically investigated the relationships between these two parameters and CF performance, using two publicly available data sets, EachMovie and MovieLens. We conducted three experiments. The first two investigated the relationship between a particular customer's number of ratings and CF recommendation performance. The third experiment evaluated the relationship between the total number of ratings for a particular item and CF recommendation performance. We found that there are ratings thresholds below which recommendation performance increases monotonically, i.e., when the numbers of customer and item ratings are below threshold levels, CF recommendation performance is affected. In addition, once rating numbers surpass threshold levels, the value of each rating decreases. These results may facilitate operational decisions when applying CF in practice.

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Methods and metrics for cold-start recommendations

Cited by 413 publications

References 5 publications

Utilizing learning process to improve recommender system for group learning support

Utilizing learning process to improve recommender system for group learning support

Enriching one-class collaborative filtering with content information from social media

Understanding collaborative filtering parameters for personalized recommendations in e-commerce

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