Hybrid algorithms for recommending new items

Abstract

Despite recommender systems based on collaborative filtering typically outperform content-based systems in terms of recommendation quality, they suffer from the new item problem, i.e., they are not able to recommend items that have few or no ratings. This problem is particularly acute in TV applications, where the catalog of available items (e.g., TV programs) is very dynamic. On the contrary, content-based recommender systems are able to recommend both old and new items but the general quality of the recommendations in terms of relevance to the users is low. In this article we present two different approaches for building hybrid collaborative+content recommender systems, whose purpose is to produce relevant recommendations, while overcoming the new item issue. The approaches have been tested on two datasets: a version of the well-known Movielens dataset enriched with content meta-data, and an implicit dataset collected from 15'000 IPTV users over a period of six months

An Enhanced Content-Based Recommender System for Academic Social Networks

“…The four different metrics (precision, recall, fallout, and F1) were used for measuring the prediction accuracy and comparing the performance of mentioned recommender algorithms. Precision plays a great role in instances where some sets of best results are required out of several possible alternatives [36][37][38].This measurement is the share of top results that are relevant. In this study, the relevant items defined include academic items visited by users and that are rated with more than 2 stars.…”

EPG Content Recommendation in Large Scale: A Case Study on Interactive TV Platform

“…Also, EPG-content data differ significantly from VOD consumption data, since (i) the number of user feedbacks is by several order of magnitude larger than for VOD data (per user few thousand vs a couple [3]), (ii) but much noisier, (iii) the number of recommendable items is smaller at a given moment (only now or soon aired programs) (iv) and the lifetime of individual items is shorter. Therefore, one needs to preprocess user log data to extract information useful for user preference modeling.…”

“…After preprocessing, the dataset was split into a train and test sets; the latter contained 686k events of the last day. 3 To characterize the cold-start phenomenon in the dataset, we measured new item and new event ratios in the test set. We used various heuristics to group items together along various item attributes.…”

A literature review of recommender systems in the television domain

“…The most similar items are then proposed to the user. Similarity between two vectors can be expressed by several metrics or algorithms (Ricci et al, 2011), such as cosine similarity (Berkovsky et al, 2007), Singular Value Decomposition (SVD) (Antonelli and Francini, 2009), Latent Semantic Analysis (LSA) (Antonelli and Francini, 2009), Filtered Feature Augmentation (Cremonesi et al, 2011b), Similarity Injection Knn (Cremonesi et al, 2011b), and so on. 2.…”