Title
From Data Analysis to Intent-Based Recommendation: An Industrial Case Study in the Video Domain
Abstract
This work presents a comprehensive study, from an industrial perspective, of the process between the collection of raw data, and the generation of next-item recommendation, in the domain of Video-on-Demand (VoD). Most research papers focus their efforts on analyzing recommender systems on already-processed datasets, but they do not face the same challenges that occur naturally in industry, e.g., processing raw interactions logs to create datasets for testing. This paper describes the whole process between data collection and recommendation, including cleaning, processing, feature engineering, session inferring, and all the challenges that a dataset provided by an industrial player in the domain posed. Then, a comparison on the new dataset of several intent-based recommendation techniques in the next-item recommendation task follows, studying the impact of different factors like the session length, and the number of previous sessions available for a user. The results show that taking advantage of the sequential data available in the dataset benefits recommendation quality, since deep learning algorithms for session-aware recommendation are consistently the most accurate recommenders. Lastly, a summary of the different challenges in the VoD domain is proposed, discussing on the best algorithmic solutions found, and proposing future research directions to be conducted based on the results obtained.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3148434
IEEE ACCESS
Keywords
DocType
Volume
Recommender systems, Task analysis, Metadata, Cleaning, Buildings, Over-the-top media services, Noise measurement, Industrial data, intent-based, session-based, user behavior
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
27
6