Recommending and evaluating choices in a virtual community of use

Hill, William C.; Stead, Larry; Rosenstein, Mark; Furnas, George W.

doi:10.1145/223904.223929

Cited by 774 publications

(400 citation statements)

References 9 publications

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“…Hill et al [9] were aware of this issue and designed a small scale experiment to measure reliability in user ratings. They carried out a two trial user study with 22 participants and a time difference of 6 weeks between trials.…”

Section: Related Workmentioning

confidence: 99%

“…Note that neither of the related surveys reviewed in the previous section [9] [4] take into account the reliability and stability of their studies. This is especially problematic in the case of Cosle's et al experiment where ratings might be separated by months.…”

Section: Measures Of Reliability In User Testsmentioning

confidence: 99%

“…These inter-test correlations are the only measures that can be compared to the works of Hill et al [9] and Cosley et al [4], with reported correlations of 0.83 and 0.70 respectively (see Section 2). However, their measures include the effect of both reliability and stability.…”

Section: Test-retest Reliability and Stabilitymentioning

confidence: 99%

See 2 more Smart Citations

I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

Amatriain

Pujol

Oliver

2009

Lecture Notes in Computer Science

153

113

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Recent growing interest in predicting and influencing consumer behavior has generated a parallel increase in research efforts on Recommender Systems. Many of the state-of-the-art Recommender Systems algorithms rely on obtaining user ratings in order to later predict unknown ratings. An underlying assumption in this approach is that the user ratings can be treated as ground truth of the user's taste. However, users are inconsistent in giving their feedback, thus introducing an unknown amount of noise that challenges the validity of this assumption.In this paper, we tackle the problem of analyzing and characterizing the noise in user feedback through ratings of movies. We present a user study aimed at quantifying the noise in user ratings that is due to inconsistencies. We measure RMSE values that range from 0.557 to 0.8156. We also analyze how factors such as item sorting and time of rating affect this noise.

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Section: Related Workmentioning

confidence: 99%

Section: Measures Of Reliability In User Testsmentioning

confidence: 99%

See 1 more Smart Citation

I Like It... I Like It Not: Evaluating User Ratings Noise in Recommender Systems

Amatriain

Pujol

Oliver

2009

Lecture Notes in Computer Science

153

113

View full text Add to dashboard Cite

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“…Collaborative systems locate peer users with a rating history similar to the current user and generate recommendations using this neighborhood. Examples include [17,21,41,46]. …”

Section: R Burkementioning

confidence: 99%

Hybrid Web Recommender Systems

Burke

Lecture Notes in Computer Science

920

525

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Abstract. Adaptive web sites may offer automated recommendations generated through any number of well-studied techniques including collaborative, content-based and knowledge-based recommendation. Each of these techniques has its own strengths and weaknesses. In search of better performance, researchers have combined recommendation techniques to build hybrid recommender systems. This chapter surveys the space of two-part hybrid recommender systems, comparing four different recommendation techniques and seven different hybridization strategies. Implementations of 41 hybrids including some novel combinations are examined and compared. The study finds that cascade and augmented hybrids work well, especially when combining two components of differing strengths. IntroductionRecommender systems are personalized information agents that provide recommendations: suggestions for items likely to be of use to a user [18,41,42]. In an ecommerce context, these might be items to purchase; in a digital library context, they might be texts or other media relevant to the user's interests. 1 A recommender system can be distinguished from an information retrieval system by the semantics of its user interaction. A result from a recommender system is understood as a recommendation, an option worthy of consideration; a result from an information retrieval system is interpreted as a match to the user's query. Recommender systems are also distinguished in terms of personalization and agency. A recommender system customizes its responses to a particular user. Rather than simply responding to queries, a recommender system is intended to serve as an information agent. 2 1 In this chapter, I use the e-commerce term "products" to refer to the items being recommended, with the understanding that other information-seeking contexts are also pertinent. 2 Techniques such as relevance feedback enable an information retrieval engine to refine its representation of the user's query, and therefore can be seen as a simple form of recommendation. The search engine Google (http://www.google.com) blurs this distinction further, using "authoritativeness" criteria in addition to strict matching [6]. R. BurkeA variety of techniques have been proposed as the basis for recommender systems: collaborative, content-based, knowledge-based, and demographic techniques are surveyed below. Each of these techniques has known shortcomings, such as the wellknown cold-start problem for collaborative and content-based systems (what to do with new users with few ratings) and the knowledge engineering bottleneck in knowledge-based approaches. A hybrid recommender system is one that combines multiple techniques together to achieve some synergy between them. For example, a collaborative system and a knowledge-based system might be combined so that the knowledgebased component can compensate for the cold-start problem, providing recommendations to new users whose profiles are too small to give the collaborative technique any traction, and the collaborative component can work it...

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“…More formally, the early ACF systems-GroupLens (Resnick et al, 1994), Ringo (Shardanand and Maes, 1995), and Video Recommender (Hill et al, 1995)-all used variants of a weighted k-nearest neighbour (NN) prediction algorithm.…”

Section: Automated Collaborative Filteringmentioning

confidence: 99%