MERGING CONSUMPTION DATA from MULTIPLE SOURCES to QUANTIFY USER LIKING and WATCHING BEHAVIOURS Sashikumar Venkataraman, Craig Carmichael Rovi Corporation
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MERGING CONSUMPTION DATA FROM MULTIPLE SOURCES TO QUANTIFY USER LIKING AND WATCHING BEHAVIOURS Sashikumar Venkataraman, Craig Carmichael Rovi Corporation Abstract further predisposed to watch certain shows and channels often based on pure habit, We describe an integrated platform that content they are already aware of, or the sheer aggregates consumption data from multiple popularity of a show, and these aspects are sources towards building a unified model for usually unaccounted in the recall score of a collaborative filtering for more accurate user recommender system. and content representations. The goal is to provide a framework that combines various In this paper we discuss a unified signals spanning explicit ratings, implicit framework that aggregates consumption data information of watching behaviors and meta- from multiple sources and fuses them with content information in a single model that meta-content to create an efficient potentially goes beyond the usual goal of recommendations framework capable of both maximizing consumption and incorporates significant discovery and high precision. First, metrics that capture “likeness” and we begin with the cold start problem where no “discovery”. We also feed the usage data consumption data is available, and create a back into meta-content to determine more baseline recommendation model that applies accurate content representations that aid in word-to-vector [6] factors solely from targeting content-based recommendations metadata content. An aggregation process is more effectively. used to accumulate these word-level vectors to content level factors for each show and channel potentially consumed. Next, we derive the usage factors for each media asset INTRODUCTION considering both explicit and implicit ratings from various sources. While the explicit Recommendation systems are becoming an ratings provide a more direct notion of increasingly key component in many media "likeness", the implicit signals only provide a and entertainment content retrieval systems by measure of watching behavior. We discuss providing a powerful, efficient method for methods to correlate these notions of likeness users to easily sift through large catalogs of and watching attributes in a more formal way. media content and finding valuable programming [1-3]. Within the last couple of A central part of the framework for decades, several algorithms have been merging consumption data from multiple developed that leverage metadata, such as sources is the Rovi Knowledge Graph that information on programs, casts and genres, incorporates factual information of all and user consumption data to provide users ‘known’ or ‘named’ things. This includes all with more targeted content recommendations movies and TV shows, music albums and through these recommendation systems [4-5]. songs, as well as all known people such as However, in most systems, much of the actors, musicians, celebrities, music bands, viewership data is typically implicit in nature, known companies and businesses, places, and models that are based on over-simplified sports teams, tournaments and players, etc. estimations of viewing behavior are often not All the facts pertaining to these entities are wholly comprehensive and intuitive. A user is synthesized from multiple publicly available 2016 Spring Technical Forum Proceedings sources such as Wikipedia, Freebase, and OVERALL ARCHITECTURE many others and correlated so as to create a unique smart tag (with a unique identifier) to We begin by describing the key steps represent each entity in the Rovi Knowledge involved in merging deep and dynamic Graph. The main utility of the knowledge metadata with usage data across various data graph for our problem is in aiding to merge sources. These steps are described in brief information across different data sources. We below and will be discussed in further detail have a merge system that can take any data in the following sections. source and does a best-effort to merge the entities in that data source with the underlying Step 1: We first merge the assets from the knowledge graph. Such a system makes the data source into the central knowledge graph, aggregation easier since different data sources to enable infusing of usage and meta- have variations in referring to entities and information to and from the knowledge graph. carry different level of meta-information. This step serves a dual purpose. Firstly, it helps in augmenting the meta-content of the One often encounters rich meta-content assets in terms of keywords, genres and deep- associated with media assets, such as genre, descriptors from the knowledge graph and keywords, and description. However, the hence aids in getting more accurate factors relevance or weight of each individual piece derived from meta-content representations. of meta-content (for finding similar movies or Secondly, it aids in merging the usage factors recommendations) is often lacking, missing or from this data source with usage factors from wrong due to multiple sources, algorithms, other data-sources that are also merged with and/or manual entry. For example, a show is a the central knowledge graph. comedy but exactly how funny and how it impacts other comedies is more of a viewing Step 2: Using word-representations, we next sentiment. Usage data, on the other hand, determine the meta-content factors provides a different kind of information in corresponding to the media-assets for cold- conveying what programs co-occur in start baseline with no or minimal usage data. watching behavior across users. Analysis of The word2vec model developed in [1] is used usage data involves very different techniques to determine the word-representations and algorithms from those for analyzing meta- corresponding to each of the meta-content content. There has been some effort in the information and these are aggregated to form context of recommendations wherein one uses a vector for each asset in K-dimensional meta-content to filter in the post-process of vector space. the collaborative filtering algorithm or mixes results coming from collaborative filtering Step 3: Next, we build a model that merges and meta-content algorithms. However, no implicit and explicit information from prior efforts make use of usage data to better multiple data sources and fuse them into the understand metadata relevance and enrich meta-content factors to create more accurate meta-content directly. It would be desirable if asset representation. This naturally results in a usage data along with the implicit/explicit model to estimate “likeness” from user- ratings of users could be leveraged to watching behavior even in absence of explicit determine the relevant weights of different information. pieces of meta-content. Step 4: Finally we feed the usage data back into meta-content in the form of coefficient weights of each individual meta-content factor involved in each asset. This enables the 2016 Spring Technical Forum Proceedings creation of more accurate representation of the associated with a genre, using movies known individual meta-content factors that further to have that genre, and then predicting them increase the precision in content-based on other movies that have a strong overlap in recommendations. keywords with the genre keywords. MERGING INTO KNOWLEDGE GRAPH The core part of building the Knowledge Graph is a “merge” function that takes in any The Rovi Knowledge Graph is a dynamic source and tries to map the entities in that system that has been created by synthesizing source with the entities in the existing graph. multiple metadata sources and evolving and Whenever a new entity, such as movie or refreshed continuously over time. Each smart personality, is merged with an existing smart tag in the Knowledge Graph has rich meta- tag, the metadata corresponding to the smart content built by a combination of manual tag gets augmented from the entity. If the new tagging and automatic aggregation from entity does not get merged with any smart tag, multiple sources along with several machine- then a new smart tag is created for the entity learning algorithms. in the KG. An important aspect of the merging is the allowance of slight variations Twitter, in the meta-content fields between the two RottenTomatoes, Facebook, assets. For example, the titles can differ Metacritic, etc. Google Trends, slightly due to several reasons. One reason for Nielsens, etc. SOCIAL REVIEWS ESPN, NFL, the difference in titles is the fact that some NBA, sources put the season number and episode Knowledge SPORTS numbers in the title, while other sources only Reuters, NEWS Graph put the episode title in the title. Sometimes CBS, NBC, they may differ due to some lexical error or BBC, OTT YouTube, absence of common words like articles. VOD Amazon, Release year could also vary by one or two + WIKIS Hulu, Vudu. units, and sometimes cast members could be be missing. All such variations are considered Wiki, Freebase, during the merge process by considering all TMDB, etc. the fields simultaneously and coming with a Fig 1: Merging of assets from multiple data sources combined match score considering all fields. into a central knowledge graph Following are 2 examples of approximate matching with inexact titles which got Fig 1 below shows the merging of matched due to matching of other fields: information from various sources into the central knowledge graph. While certain meta- BUILDING WORD-REPRESENTATIONS content fields, such as cast-members, roles, FROM META-DATA and release year