Combining Multiple Metadata Types in Movies Recommendation Using Ensemble Algorithms

Cabral, Bruno Souza; Beltrao, Renato Dompieri; Manzato, Marcelo G.; Durão, Frederico Araújo

doi:10.1145/2664551.2664569

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…However, in accordance with Mozilla's lean data collection practices, the correlation of independent data sources with browser telemetry is an undesirable solution, as it may erode client privacy. The use of ensembles for improving recommendations has also been explored [1,14,19,45,48], although these generally apply multiple models to the same or related datasets. Previous collaborative filtering models have also incorporated clustering methods [16,39,40,42], where clusters are used to define the user similarity neighborhoods.…”

Section: Related Workmentioning

confidence: 99%

Telemetry-Aware Add-on Recommendation for Web Browser Customization

Lopatka¹,

Ng²,

Miroglio³

et al. 2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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Web Extensions (add-ons) allow clients to customize their Web browsing experience through the addition of auxiliary features to their browsers. The add-on ecosystem is a market differentiator for the Firefox browser, offering contributions from both commercial entities and community developers. In this paper, we present the Telemetry-Aware Add-on Recommender (TAAR), a system for recommending add-ons to Firefox users by leveraging separate models trained to three main sources of user data: the set of add-ons a user already has installed; usage and interaction data (browser Telemetry); and the language setting of the user's browser (locale). We build individual recommendation models for each of these data sources, and combine the recommendations they generate using a linear stacking ensemble method. Our method employs a novel penalty function for tuning weight parameters, which is adapted from the log likelihood ratio cost function, allowing us to scale the penalty of both correct and incorrect recommendations using the confidence weights associated with the individual component model recommendations. This modular approach provides a way to offer relevant personalized recommendations while respecting Firefox's granular privacy preferences and adhering to Mozilla's lean data collection policy. To evaluate our recommender system, we ran a large-scale randomized experiment that was deployed to 350,000 Firefox users and localized to 11 languages. We found that, overall, users were 4.4% more likely to install add-ons recommended by our ensemble method compared to a curated list. Furthermore, the magnitude of the increase varies significantly across locales, achieving over 8% improvement among German-language users. CCS CONCEPTS • Information systems → Personalization; Web applications; Recommender systems; • Human-centered computing → Webbased interaction.

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Section: Related Workmentioning

confidence: 99%

Telemetry-Aware Add-on Recommendation for Web Browser Customization

Lopatka¹,

Ng²,

Miroglio³

et al. 2019

Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization

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“…Cabral et al [13] proposed three ensemble strategies that combine predictions from a recommender trained with distinct item metadata into a unified rank of recommended items. In comparison, da Costa at al.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Multiple User Interactions for Ranking Personalization Using Ensemble Methods

Durão¹,

Cabral²,

Beltrao³

et al. 2018

International Conferences on Software Engineering and Knowledge Engineering

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The variety of interaction paradigms on the Web, such as clicking, commenting or rating are important sources that help recommender systems to gather accurate information about users' preferences. Ensemble methods can be used to combine all these pieces of information in a post-processing step to generate recommendations that are more relevant. In this paper, we review the application of existing ensemble methods to improve ranking recommendations in the multimodal interactions context. We compared four ensemble strategies, ranging from simple to complex algorithms including Gradient Descent and Genetic Algorithm to find optimal weights. The evaluation using the HetRec 2011 MovieLens 2k dataset with three different types of interactions shows that a considerable 7% improvement in the Mean Average Precision can be achieved using ensembles when compared to the most performant single interaction.

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Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

Sivanaiah¹,

Milton²,

Mirnalinee³

2020

IDA

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The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top-n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system.

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Combining Multiple Metadata Types in Movies Recommendation Using Ensemble Algorithms

Cited by 3 publications

References 16 publications

Telemetry-Aware Add-on Recommendation for Web Browser Customization

Telemetry-Aware Add-on Recommendation for Web Browser Customization

Evaluating Multiple User Interactions for Ranking Personalization Using Ensemble Methods

Content boosted hybrid filtering for solving pessimistic user problem in recommendation systems

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