Recommender systems and their impact on sales diversity

Fleder, Daniel M.; Hosanagar, Kartik

doi:10.1145/1250910.1250939

Cited by 127 publications

(69 citation statements)

References 20 publications

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“…Indeed, Bodapati (2008) showed that product awareness is a necessary condition for actual purchase. Further, according to Fleder and Hosanger (2007), recommendation agents impact sales diversity. Intuitively, preference relaxation allows for the consideration of products that are filtered out when using logical filtering approaches.…”

Section: Hypothesesmentioning

confidence: 99%

Intelligent product search with soft-boundary preference relaxation

Dąbrowski

Acton

Heijden

2012

Expert Systems with Applications

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Abstract. This paper proposes a novel method for preference relaxation in online product search, which enables consumers to make quality choices without suffering from the commonly experienced information overload. In online shopping scenarios that involve multi-attribute choice tasks, it can be difficult for consumers to process the vast amounts of information available and to make satisfactory buying decisions. In such situations consumers are likely to eliminate potentially good choices early on, using hard-constraint filtering tools. Our approach uses edge sets to identify the alternatives on the soft boundary and the principle of alternative domination to suppress the alternatives on this boundary that are irrelevant. We demonstrate how our approach outperforms existing methods for product search in a set of simulations using two sets of 2650 car advertisements and 1813 digital cameras gathered from a popular online store.

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Section: Hypothesesmentioning

confidence: 99%

Intelligent product search with soft-boundary preference relaxation

Dąbrowski

Acton

Heijden

2012

Expert Systems with Applications

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“…If a paper only describes the authors' previous work, then we replaced it with the previous papers of the same authors. We also excluded problem-raising papers that accentuated the im-portance of beyond-accuracy aspects based on real-world data [5] or simulation [22]. Ultimately, we selected 72 papers for review.…”

Section: Selection Of Surveyed Papersmentioning

confidence: 99%

“…It is well known that collaborative filtering (CF), one of the popular algorithms in the literature of recommendation systems, is accurate, but it recommends users the items similar to each other. Relatively recently, studies have repeatedly pointed out that beyond-accuracy aspects-such as the diversity and novelty of the recommended items-should be considered alongside accuracy [22], [45].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Recommendation Methods Beyond Accuracy

Han

Yamana

2017

IEICE Trans. Inf. & Syst.

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SUMMARYIn recommending to another individual an item that one loves, accuracy is important, however in most cases, focusing only on accuracy generates less satisfactory recommendations. Studies have repeatedly pointed out that aspects that go beyond accuracy-such as the diversity and novelty of the recommended items-are as important as accuracy in making a satisfactory recommendation. Despite their importance, there is no global consensus about definitions and evaluations regarding beyondaccuracy aspects, as such aspects closely relate to the subjective sensibility of user satisfaction. In addition, devising algorithms for this purpose is difficult, because algorithms concurrently pursue the aspects in trade-off relation (i.e., accuracy vs. novelty). In the aforementioned situation, for researchers initiating a study in this domain, it is important to obtain a systematically integrated view of the domain. This paper reports the results of a survey of about 70 studies published over the last 15 years, each of which addresses recommendations that consider beyond-accuracy aspects. From this survey, we identify diversity, novelty, and coverage as important aspects in achieving serendipity and popularity unbiasedness-factors that are important to user satisfaction and business profits, respectively. The five major groups of algorithms that tackle the beyond-accuracy aspects are multi-objective, modified collaborative filtering (CF), clustering, graph, and hybrid; we then classify and describe algorithms as per this typology. The off-line evaluation metrics and user studies carried out by the studies are also described. Based on the survey results, we assert that there is a lot of room for research in the domain. Especially, personalization and generalization are considered important issues that should be addressed in future research (e.g., automatic per-user-trade-off among the aspects, and properly establishing beyond-accuracy aspects for various types of applications or algorithms).

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“…However, this method is difficult to apply in "user-to-user" recommendation systems due to the lack of user information and explicit user interests. Collaborative filtering is a popular recommendation whose idea is to discover similar users and recommend their commonly interested items by generating a comparable weighted recommendation value [7]. D. Agarose et al and Y. Koren adopted latent factor matrix decomposition to collaborative filtering [1,2,10].…”

Section: Related Workmentioning

confidence: 99%

A personalized recommendation system for NetEase dating site

et al. 2014

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With the popularity of internet, more and more people try to find friends or dating partners on online dating web sites. Recommending appropriate partners from a large amount of candidates becomes an interesting and challenging problem in the field of recommendation system. Various types of recommendation techniques (e.g., content based recommendation, collaborative filtering and association rule mining) have be proposed to tackle this problem. However most of them ignore the personalization concerns that they (1) mainly consider the hot users or frequent items, (2) cover only a portion of users especially ignoring the long tails, (3) and cannot deal with the cold start problem properly. In this paper, we present a regression based hybrid recommendation system that makes use of matching degree, fancy degree, activity, sincerity, popularity and enthusiasm, to recommend appropriate partners. The experimental evaluation of our recommendation system on a real dating web site shows our strategy is more effective and efficient than its previous version which follows the principle of giving higher priority to the recent active users.

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Recommender systems and their impact on sales diversity

Cited by 127 publications

References 20 publications

Intelligent product search with soft-boundary preference relaxation

Intelligent product search with soft-boundary preference relaxation

A Survey on Recommendation Methods Beyond Accuracy

A personalized recommendation system for NetEase dating site

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