Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

Anava, Oren; Golan, Shahar; Golbandi, Nadav; Karnin, Zohar; Lempel, Ronny; Rokhlenko, Oleg; Somekh, Oren

doi:10.1145/2736277.2741109

Cited by 21 publications

(50 citation statements)

References 23 publications

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“…[8], [9] are works which also address the item cold-start problem in an active learning scheme. However, they both focus on the pure collaborative filtering model and do not consider the content information either.…”

Section: Active Learning In Recommender Systemsmentioning

confidence: 99%

“…Our goal is to generate accurate rating predictions on the testing items. Following [8], [9], the trainingtesting experiments are done once (also called holdout [51]). Inspired by [30], we randomly select half of all users as the activeselection set and the remaining users form the prediction set.…”

Section: Datasetmentioning

confidence: 99%

“…However, limited works have been conducted to address the item cold-start problem by active learning. [8], [9] use the active learning idea but ignore items' attribute information. Meanwhile, they select users based on limited criteria.…”

Section: Introductionmentioning

confidence: 99%

“…• Dynamic active learning budget. In previous active learning works [9], [10], the budget of active learning (i.e. the number of users selected for rating requests) for a new item is fixed.…”

mentioning

confidence: 99%

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Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning

Zhu

Lin

et al. 2020

IEEE Trans. Knowl. Data Eng.

105

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In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.

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Section: Active Learning In Recommender Systemsmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning

Zhu

Lin

et al. 2020

IEEE Trans. Knowl. Data Eng.

105

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“…Aleksandrova, et al [9] present a hybrid matrix factorisation model representing users and items. Anava, et al [14] propose efficient optimal design based algorithms to solve budget-constrained item cold-start.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy prototype classifier based on items and its application in recommender system

Mei¹,

Gong²,

Li³

2017

IJCIS

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Currently, recommender systems (RS) are incorporating implicit information from social circle of the Internet. The implicit social information in human mind is not easy to reflect in appropriate decision making techniques. This paper consists of 2 contributions. First, we develop an item-based prototype classifier (IPC) in which a prototype represents a social circle's preferences as a pattern classification technique. We assume the social circle which distinguishes with others by the items their members like. The prototype structure of the classifier is defined by two2-dimensional matrices. We use information gain and OWA aggregator to construct a feature space. The item-based classifier assigns a new item to some prototypes with different prototypicalities. We reform a typical data set-Iris data set in UCI Machine Learning Repository to verify our fuzzy prototype classifier. The second proposition of this paper is to give the application of IPC in recommender system to solve new item cold-start problems. We modify the dataset of MovieLens to perform experimental demonstrations of the proposed ideas.

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Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

Parambath

Chawla

2020

Data Min Knowl Disc

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Recommender systems are widely used in online platforms for easy exploration of personalized content. The best available recommendation algorithms are based on using the observed preference information among collaborating entities. A significant challenge in recommender system continues to be item cold-start recommendation: how to effectively recommend items with no observed or past preference information. Here we propose a two-stage algorithm based on soft clustering to provide an efficient solution to this problem. The crux of our approach lies in representing the items as soft-cluster embeddings in the space spanned by the side-information associated with the items. Though many item embedding approaches have been proposed for item cold-start recommendations in the past—and simple as they might appear—to the best of our knowledge, the approach based on soft-cluster embeddings has not been proposed in the research literature. Our experimental results on four benchmark datasets conclusively demonstrate that the proposed algorithm makes accurate recommendations in item cold-start settings compared to the state-of-the-art algorithms according to commonly used ranking metrics like Normalized Discounted Cumulative Gain (NDCG) and Mean Average Precision (MAP). The performance of our proposed algorithm on the MovieLens 20M dataset clearly demonstrates the scalability aspect of our algorithm compared to other popular algorithms. We also propose the metric Cold Items Precision (CIP) to quantify the ability of a system to recommend cold-start items. CIP can be used in conjunction with relevance ranking metrics like NDCG and MAP to measure the effectiveness of the cold-start recommendation algorithm.

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Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design

Cited by 21 publications

References 23 publications

Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning

Addressing the Item Cold-Start Problem by Attribute-Driven Active Learning

Fuzzy prototype classifier based on items and its application in recommender system

Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations

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