Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Journal of Machine Learning Research
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
From hits to niches?: or how popular artists can bias music recommendation and discovery
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Metrics for evaluating the serendipity of recommendation lists
JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Networks, Crowds, and Markets: Reasoning About a Highly Connected World
Optimizing multiple objectives in collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
The Filter Bubble: What the Internet Is Hiding from You
The Filter Bubble: What the Internet Is Hiding from You
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A model for serendipitous music retrieval
Proceedings of the 2nd Workshop on Context-awareness in Retrieval and Recommendation
Spotting trends: the wisdom of the few
Proceedings of the sixth ACM conference on Recommender systems
Ads and the city: considering geographic distance goes a long way
Proceedings of the sixth ACM conference on Recommender systems
Burst the filter bubble: using semantic web to enable serendipity
ISWC'12 Proceedings of the 11th international conference on The Semantic Web - Volume Part II
User-centric evaluation of a K-furthest neighbor collaborative filtering recommender algorithm
Proceedings of the 2013 conference on Computer supported cooperative work
Leveraging microblogs for spatiotemporal music information retrieval
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
Cache-conscious performance optimization for similarity search
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Penguins in sweaters, or serendipitous entity search on user-generated content
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Catch-up TV recommendations: show old favourites and find new ones
Proceedings of the 7th ACM conference on Recommender systems
Escape the bubble: guided exploration of music preferences for serendipity and novelty
Proceedings of the 7th ACM conference on Recommender systems
On weighted hybrid track recommendations
ICWE'13 Proceedings of the 13th international conference on Web Engineering
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
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Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that - in contrast to previous work - attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of "serendipitous discovery", we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist's emphasis on serendipity indeed improves user satisfaction.