Addressing the New Item problem in video recommender systems by incorporation of visual features with restricted Boltzmann machines. Issue 3 (19th October 2020)

• Record Type: Journal Article
• Authors: Hazrati, Naieme; Elahi, Mehdi
• Is Part Of: Expert systems. Volume 38:Issue 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-19
• Subjects: cold start -- multimedia -- new item -- recommender systems -- visually aware; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12645
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Over the past years, the research of video recommender systems (RSs) has been mainly focussed on the development of novel algorithms. Although beneficial, still any algorithm may fail to recommend video items that the system has no form of data associated to them (New Item Cold Start). This problem occurs when a new item is added to the catalogue of the system and no data are available for that item. In content‐based RSs, the video items are typically represented by semantic attributes, when generating recommendations. These attributes require a group of experts or users for annotation, and still, the generated recommendations might not capture a complete picture of the users' preferences, for example, the visual tastes of users on video style. This article addresses this problem by proposing recommendation based on novel visual features that do not require human annotation and can represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios. Abstract : This article addresses cold start problem by proposing recommendations based on novel visual features that do not require human annotation and represent visual aspects of video items. We have designed a novel evaluation methodology considering three realistic scenarios, that is, (a) extreme cold start, (b) moderate cold start, and (c) warm‐start scenario. We have conducted a set of comprehensive experiments, and our results have shown the superior performance of recommendations based on visual features, in all of the evaluation scenarios.