With the World Wide Web moving from passive to active, the role of recommender systems as an aid to make decisions play a very prominent role. This enables its users to nd new items of high personal interest, which they were previously unaware of. While traditional ap- proaches have shown the generation of high quality recommendations, the additional use of background knowledge to describe the items and their preferences on a more granular level is still lacking. Furthermore, these approaches do not take into consideration the contextual infor- mation, wherein the dimension 'time' plays a signicant role. In this paper, we propose a new approach for recommending movies, which se- mantically enriches the process of generating recommendations by using a taxonomy derived out of dierent data sources from the LOD-Cloud. Furthermore, the paper also addresses the interplay between the rating behavior of the users and the dimension 'time'.

1. Towards Time-Aware Semantic enriched
Recommender Systems for movies
Marko Harasic, Piere Ahrendt, Alexandru Todor, Adrian Paschke
The 9th International Web Rule Symposium (RuleML)
Rule-based Recommender Systems for the Web of Data
Berlin, 5th August 2015

2. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
Introduction
● Traditional recommender lack the
usage of contextual information
● Features of items are related to each
other
● User preferences change over time
but still have an subtle influence
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3. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
Data
● Movielens 1M dataset
○ Enriched with DBpedia-URIs
○ URIs for mapping items to LOD
● Categories as attributes from:
○ DBpedia
○ Freebase
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4. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
● Combines two systems
● Profiles compared by
their prefered attributes
Approach
Hybrid recommender
Collaborative Filtering Content Based Filtering
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5. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
Approach
taxonomy support
● Item representations consist of:
○ Directly extracted attributes
■ TF-IDF Weighted
○ Virtual attributes
■ Superclasses of categories
■ Weighted depending on their height
Genre
SciFi
Cyber
Punk
Dystopic
Drama
Blade Runner
0.7 0.5
0.30.2
0.05
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6. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
Approach
time awereness
● Preferences change over time
○ Older preferences dampened by
exponentional decay factor
● Smaller values keep memories
● Larger values prefer more recent data
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7. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
● 5 Fold-Cross validation
● F1-Metric
● BBI2I-CF with adjusted cosine as
baseline
● Determination of optimal Values:
○ Neighbourhood
○ Time decay
○ Hierarchy Influence
Evaluation
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8. Towards Time-Aware Semantic enriched Recommender Systems for movies Marko Harasic
Conclusion and
future work
● Conclusion:
○ Approach has higher accurary then I2I-CF
○ Automatic extracted Taxonomy improves accuracy
○ Incorporation of time is benificial
● Future work:
○ Application on the music domain
○ Improved time degredation formula
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9. Questions?
Towards Time-Aware Semantic enriched
Recommender Systems for movies
Marko Harasic
harasic@inf.fu-berlin.de
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