Collaborative recommendations with content-based filters for cultural activities via a scalable event distribution platform

Abstract

Nowadays, most people have limited leisure time and the offer of (cultural) activities to spend this time is enormous. Consequently, picking the most appropriate events becomes increasingly difficult for end-users. This complexity of choice reinforces the necessity of filtering systems that assist users in finding and selecting relevant events. Whereas traditional filtering tools enable e.g. the use of keyword-based or filtered searches, innovative recommender systems draw on user ratings, preferences, and metadata describing the events. Existing collaborative recommendation techniques, developed for suggesting web-shop products or audio-visual content, have difficulties with sparse rating data and can not cope at all with event-specific restrictions like availability, time, and location. Moreover, aggregating, enriching, and distributing these events are additional requisites for an optimal communication channel.In this paper, we propose a highly-scalable event recommendation platform which considers event-specific characteristics. Personal suggestions are generated by an advanced collaborative filtering algorithm, which is more robust on sparse data by extending user profiles with presumable future consumptions. The events, which are described using an RDF/OWL representation of the EventsML-G2 standard, are categorized and enriched via smart indexing and open linked data sets. This metadata model enables additional content-based filters, which consider event-specific characteristics, on the recommendation list. The integration of these different functionalities is realized by a scalable and extendable bus architecture. Finally, focus group conversations were organized with external experts, cultural mediators, and potential end-users to evaluate the event distribution platform and investigate the possible added value of recommendations for cultural participation.

Selection and Ranking of Activities in the Social Web

“…PITTCULT [8] is a collaborative filtering recommender for cultural events, based on trust relations among users for finding similar users. Another collaborative filtering recommender is CUPID [13], which recommends events for the Flemish cultural scene. Similarly to us, they have a pool of events to be recommended, and they apply content and spatial filters after the recommendation process in order to exclude non-compatible events.…”

Event Recommendation in Social Networks with Linked Data Enablement

“…They build a complete event tracking system which is open to the integration of heterogeneous information resources. De Pressemier et al (De Pessemier et al, 2011;Coppens et al, 2012) focus on representation of events as structured data. They build a highly-scalable event recommendation platform for cultural events, which are collected and published as Linked Open Data with an RDF/OWL representation using the EventsML-G2 standard.…”

“…To address this problem, recommendation models (Cornelis et al, 2005;Kayaalp et al, 2009;Klamma et al, 2009;Konstas et al, 2009;Coppens et al, 2012;Minkov et al, 2010;Li et al, 2010;Daly and Geyer, 2011;De Pessemier et al, 2011) are designed to select relevant events that are most likely of interest to each individual user. A general approach of event recommendation is content based (Cornelis et al, 2005;De Pessemier et al, 2011), which aims to capture descriptive features of an event such as location, time and theme to match user interests. To characterize user interests, content-based approach leverage the past event attendance records of a user, as well as the user feedback such as the rating of events.…”

A Personalized and Context-Aware News Offer for Mobile Devices

“…cold start problem) [11]. CF requires a critical amount of consumptions (explicit or implicit feedback) before an item can be recommended.…”