![]() ![]() Examples of the identified domains include fashion, tourism, food, media streaming, and e-commerce. Moreover, for each media type, we discuss various domains in which multimedia content plays a key role in human decision-making and is therefore considered in the recommendation process. The aim of this survey is to present a thorough review of the state-of-the-art of recommender systems that leverage multimedia content, by classifying the reviewed papers with respect to their media type, the techniques employed to extract and represent their content features, and the recommendation algorithm. In fact, scientific research related to the analysis of multimedia content has made possible effective content-based recommender systems capable of suggesting items based on an analysis of the features extracted from the item itself. Today's recommender systems suggest items of various media types, including audio, text, visual (images), and videos. Recommender systems have become a popular and effective means to manage the ever-increasing amount of multimedia content available today and to help users discover interesting new items. We hope this chapter provides fresh insights into the nexus of multimedia and recommender systems, which could be exploited to broaden the frontier in the field. Throughout this work, we mentioned several use-cases of MMRSs in the recommender systems research across several domains or products types such as food, fashion, music, videos, and so forth. Finally, we discuss recent state-of-the-art MMRS algorithms, which we classify and present according to classical hybrid models (e.g., VBPR), neural approaches, and graph-based approaches. ![]() ![]() We then discuss the most popular multimedia content processing approaches to produce item representations that may be utilized as side information in an MMRS. ![]() We first outline the key considerations and challenges that must be taken into account while developing an MMRS. The multimedia features are then used by an MMRS to recommend either (1) media items from which the features were derived, or (2) non-media items utilizing the features obtained from a proxy multimedia representation of the item (e.g., images of clothes). This chapter studies state-of-the-art research related to multimedia recommender systems (MMRS), focusing on methods that integrate multimedia content as side information to various recommendation models. DOWNLOAD IGLASSES MOVIEMovie, user-generated video Local features SIFT/SURF Fashion, points-of-interest, tags, social media, search query, generic image, movie, user-generated video LBP Eye-glasses, make-up HOG Eye-glasses, make-up, tourism, photography DWT/DFT Generic images, tags, eye-glasses Pretrained or fine-tuned CNN Caffe reference model Fashion AlexNet Social curation network, dance background, movie ResNet Fashion VGG Food, tourism, movie GoogleNet Movie VGG-face Police photo lineup DenseNet Fashion clothes Places-CNN Geolocated images, movies Aesthetic network Fashion clothes Siamese-GoogleNet Fashion clothes Siamese-AlexNet Fashion clothes, generic images, photography, user-generated video GMM Photography, movie User-generated video Graph-based Venue, generic image in practice, the position of data fusion in the pipeline can differ based on the fusion type. ![]()
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