diversification has become a leading topic because it can solve the over-fitting problem and improve user experience
all RSs only consider items that have not been consumed by the user
user feedback collection
explicit: ask user to provide rating
implicitly: track user’s activity
general RS research challenges
data sparsity: users wont rate every item in the catalog
cold start: hard to deal with new users or content
big data: limits types of algorithms that can be used
over-fitting/overspecialization: keep recommending items that the user likes, but not capturing the user’s full preferences
diversity definitions
average dissimilarity between all pairs of items in the result set (CURRENTLY PREFERRED)
Gini coefficient - measure of distributional inequality
diversity between two items is based on item relevance, similarity, and places in the ranked list
user perceived diversity questionnaire
diversity part of nDCG (net discounted cumulative gain) measure
diversity presented as a nDCG measure
combination of genre coverage (how many genres present in ranked list) and non-redundancy (genres do not repeat on the list) -> (shows promise to be improved with more metadata)
evaluation methods
measure diversity, similarity, and relative gain on 7 RSs on a 1000 job advert dataset
simulated environment (gini)
measured diversity on MovieLens 1M dataset with several algos
20 participants answered a questionnaire about diversity of recommendations
calculated diversity on MovieLens and last.fm dataset
last.fm
movielens and netflix
250 volunteers evaluated the diversity of presented recommendations using a 7 point likert scale
diversity algorithms
re-ranking recommendation list using: item popularity, reverse predicted rating value, item average rating, item absolute likeability, item relative likeability
change distribution of rec. items in terms of popularity
with 1% precision loss, increased percentage of long tail recs from 16% to 32%
maximization of parameterized combined objective function representing accuracy/diversity tradeoff, using greedy relaxation and quantization algorithms
increased diversity, measured by evaluation of precision and recall against the novelty of recommendations
diversity determinant is genre difference among films
evaluated algos with users
users noticed high-diversity items, found them interesting
ClusDiv: items are clustered rec. list built by selecting items from different clusters; aims to maximize diversity levels on the rec. lists
increased diversity with no impact on accuracy (z-diversity and accuracy using recall)
SM - probabilistic specification maximizer model
increased diversity measured as inter-list diversity
Pareto-efficient multi-objective ranking and rec. list build
increased diversity: diversity measured by distance, accuracy measured by precision and recall
experiment showed that participant personality did not affect recommendations diversity
experiment showed that diversity positively impacted user satisfaction
https://www.sciencedirect.com/science/article/abs/pii/S0950705117300680