On Unexpectedness in Recommender Systems

@article{Adamopoulos2014OnUI,
  title={On Unexpectedness in Recommender Systems},
  author={Panagiotis Adamopoulos and Alexander Tuzhilin},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  year={2014},
  volume={5},
  pages={1 - 32}
}
Although the broad social and business success of recommender systems has been achieved across several domains, there is still a long way to go in terms of user satisfaction. One of the key dimensions for significant improvement is the concept of unexpectedness. In this article, we propose a method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics. In particular, we propose a new concept of unexpectedness as recommending to users… 
Novel Perspectives in Collaborative Filtering Recommender Systems
TLDR
The overall goal of this research program is to move the focus from even more accurate rating predictions to a holistic experience to the users by avoiding the over-specialization and concentration problems and providing the users with non-obvious but high quality personalized recommendations that they will remarkably like.
Novel Perspectives in Collaborative Filtering Recommender Systems
TLDR
The overall goal of this research program is to move the focus from even more accurate rating predictions to offering a holistic experience to the users by avoiding the over-specialization and concentration problems and providing the users with non-obvious but high quality personalized recommendations that they will remarkably like.
What is a Fair Value of Your Recommendation List?
TLDR
A general criterion, named “audience satisfaction”, is constructed, which thoroughly describe the result of interaction between users and recommendation service, and takes into account the set of requirements, which are important for business application of a recommender system.
Exploring the Potential of the Resolving Sets Model for Introducing Serendipity to Recommender Systems
TLDR
This work proposes a novel serendipity-oriented user modeling method, based on graph-theory approach - resolving sets in a graph, which enables findingserendipitous items in a multi-dimensional content-based space by detecting the expected items for the user.
Should I Follow the Crowd?: A Probabilistic Analysis of the Effectiveness of Popularity in Recommender Systems
TLDR
A crowdsourced dataset devoid of the usual biases displayed by common publicly available data is built, in which contradictions between the accuracy that would be measured in a common biased offline experimental setting, and the actual accuracy that can be measured with unbiased observations are illustrated.
Investigating serendipity in recommender systems based on real user feedback
TLDR
It is found that most kinds of serendipity and all the variations of serentipity components broaden user preferences, but one variation of unexpectedness hurts user satisfaction.
Introducing serendipity in recommender systems through collaborative methods
TLDR
A new hybrid algorithm that combines a standard user-based collaborative filtering method, and item attributes has been proposed to improve the quality of serendipity over those that use item ratings alone.
A Scalable Clustering Algorithm for Serendipity in Recommender Systems
TLDR
This paper addresses the first issue of high sparsity in CF by modifying the popular parallel seeding technique proposed by Bahmani et al. for use in a spherical setting, and effectuate serendipity in movie recommender systems with an end-to-end algorithm, Serendipitous Clustering for Collaborative Filtering (SC-CF).
Customizable Surprising Recommendation Based on the Tradeoff between Genre Difference and Genre Similarity
  • Qianru Zheng, H. Ip
  • Computer Science
    2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology
  • 2012
TLDR
The proposed framework, called Customizable GenPref, is a framework of making recommendations such that by tuning a user-defined parameter a a user will receive recommendations which are either similar to his/her previous choices, or different and novel that surprises him/her, or combinations of both.
Beyond rating prediction accuracy: on new perspectives in recommender systems
TLDR
The focus from even more accurate rating predictions is moved to offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.
...
...

References

SHOWING 1-10 OF 133 REFERENCES
On Unexpectedness in Recommender Systems: Or How to Expect the Unexpected
TLDR
A model to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics is proposed and a new concept of unexpectedness is proposed as recommending to users those items that depart from what they expect from the system.
Metrics for Evaluating the Serendipity of Recommendation Lists
In this paper we propose metrics unexpectedness and unexpectedness_r for measuring the serendipity of recommendation lists produced by recommender systems. Recommender systems have been evaluated in
On discovering non-obvious recommendations: using unexpectedness and neighborhood selection methods in collaborative filtering systems
TLDR
A new probabilistic neighborhood-based method is proposed as an improvement of the standard $k$-nearest neighbors approach, alleviating some of the most common problems of collaborative filtering recommender systems, based on classical metrics of dispersion and diversity as well as some newly proposed metrics.
Rank and relevance in novelty and diversity metrics for recommender systems
TLDR
A formal framework for the definition of novelty and diversity metrics is presented that unifies and generalizes several state of the art metrics and identifies three essential ground concepts at the roots of noveltyand diversity: choice, discovery and relevance, upon which the framework is built.
Novelty and Diversity in Top-N Recommendation -- Analysis and Evaluation
TLDR
It is argued that the motivation of diversity research is to increase the probability of retrieving unusual or novel items which are relevant to the user and a methodology to evaluate their performance in terms of novel item retrieval is introduced.
Avoiding monotony: improving the diversity of recommendation lists
TLDR
This work model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem, leading to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution.
Beyond accuracy: evaluating recommender systems by coverage and serendipity
TLDR
It is argued that the new ways of measuring coverage and serendipity reflect the quality impression perceived by the user in a better way than previous metrics thus leading to enhanced user satisfaction.
Beyond rating prediction accuracy: on new perspectives in recommender systems
TLDR
The focus from even more accurate rating predictions is moved to offering a holistic experience to the users by avoiding the over-specialization of generated recommendations and providing the users with sets of non-obvious but high quality recommendations that fairly match their interests and they will remarkably like.
TopRecs+: Pushing the Envelope on Recommender Systems
TLDR
In section 2, it is shown how better scalability can be achieved in both aspects for one of the most popular and practical recommendation algorithms.
Temporal diversity in recommender systems
TLDR
It is shown that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey, and proposed and evaluated set methods that maximise temporal recommendation diversity without extensively penalising accuracy.
...
...