Does Wikipedia Information Help Netflix Predictions?

Lees-Miller, John; Anderson, Ferguson; Hoehn, Barbara J; Greiner, Russell

doi:10.1109/icmla.2008.121

Cited by 14 publications

(7 citation statements)

References 6 publications

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“…The results are fairly comparable to our approach. The orange curve with diamond markers exploits Wikipedia to feed a content-based recommender system, as proposed in [5]. Finally, the green curve with dash markers refers to a CB recommender system leveraging DBpedia [9].…”

Section: Discussionmentioning

confidence: 99%

“…They leverage only text information sources, while we exploit structured and disambiguated information contained within RDF triples. In [5] the authors use the text content and the hyperlink structure of Wikipedia pages to identify similarities between movies. The aim is to check whether Wikipedia may improve the results of recommendations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Exploiting the web of data in model-based recommender systems

Noia

Mirizzi

Ostuni

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and Linked-MDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -both content-based and collaborative filtering ones.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Exploiting the web of data in model-based recommender systems

Noia

Mirizzi

Ostuni

et al. 2012

Proceedings of the Sixth ACM Conference on Recommender Systems

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“…Web Directory. Wikipedia was used to estimate similarity between movies [20] in order to provide recommendations for the Netflix Prize competition by using a k-nearest neighbor and a pseudo-SVD algorithm. In [47], an approach for filtering RSS feeds and e-mails is presented, which makes use of Wikipedia to automatically generate the user profiles from the user's document collection.…”

Section: Semantic Information For User and Item Representationsmentioning

confidence: 99%

Combining content with user preferences for non-fiction multimedia recommendation: a study on TED lectures

Pappas

Popescu-Belis

2014

Multimed Tools Appl

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This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 profiles of users, who have made more than 100,000 ratings and 200,000 comments. The corresponding metadata, which we make available, can be used for training and testing generic or personalized recommender systems. We define content-based, collaborative, and combined recommendation methods for TED lectures and use cross-validation to select the best parameters of keyword-based (TFIDF) and semantic vector space-based methods (LSI, LDA, RP, and ESA). We compare these methods on a personalized recommendation task in two settings, a cold-start and a non-cold-start one. In the cold-start setting, semantic vector spaces perform better than keywords. In the noncold-start setting, where collaborative information can be exploited, content-based methods are outperformed by collaborative filtering ones, but the proposed combined method shows acceptable performances, and can be used in both settings. For the generic recommendation task, LSI and RP again outperform TF-IDF.Keywords Content-based multimedia indexing · Recommender systems · Multimedia recommendation · TED lectures We are grateful for the support received for this work from the European Union through the inEvent project FP7-ICT n. 287872 (see http://www.inevent-project.eu).

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“…Based on certain feature representation methods, the similarity measure between users and videos is the basis for recommendation. Miller et al [15] used the content and hyperlink structure of Wikipedia to define the similarity between two movies and used the knearest neighbor algorithm for movie recommendation. Experiments showed that the knowledge of Wikipedia did not significantly improve the performance of the recommender system.…”

Section: Introductionmentioning

confidence: 99%

A Enhancing Video Recommendation of Multi-Headed Self-Attentive with Multimodal Features

Chan

Liddle

Pera

et al. 2023

Preprint

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The exponential growth of the internet has led to the creation of a large volume of information, making it challenging for users to navigate through. As a solution to this challenge, recommendation systems have emerged. In video recommendation, besides applying some basic interaction data (including image data, behavior data, context data, etc.) to the recommendation model, many studies also try to apply the video content data to the model for video recommendation. In this paper, we propose different types of features for different modal data, and select the most suitable feature types according to different tasks. The internal representation of features is learned by a multi-headed self-attentive mechanism and the cross-representation of features is learned by attention. On the basis of this, the multimodal features of video data are represented and modeled in a unified way, which is the basis for the implementation of multi-view video recommendation. and based on this, we add multimodal video content to mine richer and more comprehensive descriptions, so as to provide more accurate and personalized recommendations. The results of experiments conducted on real data sets demonstrate the effectiveness of the model proposed in this paper.

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Does Wikipedia Information Help Netflix Predictions?

Abstract: Abstract

Cited by 14 publications

References 6 publications

Exploiting the web of data in model-based recommender systems

Exploiting the web of data in model-based recommender systems

Combining content with user preferences for non-fiction multimedia recommendation: a study on TED lectures

A Enhancing Video Recommendation of Multi-Headed Self-Attentive with Multimodal Features

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