Publication

Combining Content with User Preferences for Non-Fiction Multimedia Recommendation: A Study on TED Lectures

Andrei Popescu-Belis, Nikolaos Pappas
2015
Journal Articles

Abstract

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 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 non-cold-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.

Official source

https://infoscience.epfl.ch/entities/publication/47f79427-0993-4e56-857a-a819c64f7631

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Andrei Popescu-Belis, Nikolaos Pappas
2015
Journal Articles

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/47f79427-0993-4e56-857a-a819c64f7631

About this result

Related concepts (18)

Recommender system

A recommender system, or a recommendation system (sometimes replacing 'system' with a synonym such as platform or engine), is a subclass of information filtering system that provide suggestions for items that are most pertinent to a particular user. Typically, the suggestions refer to various decision-making processes, such as what product to purchase, what music to listen to, or what online news to read. Recommender systems are particularly useful when an individual needs to choose an item from a potentially overwhelming number of items that a service may offer.

Collaborative filtering

Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue than that of a randomly chosen person.

Personalization management system

A personalization management system (PMS) is an integrated software solution that enables users in an organization to manage and deliver personalized messages, campaigns, and interactive experiences to consumers across different communications channels and devices. The term PMS was first used in a 2003 study on personalization, but it was later popularized by the startup Croct, which was the first company to use the term PMS to distinguish the emerging category of platforms and technologies focused on delivering personalized customer experiences.

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