reupdate

docs/lenskit.bib

@@ -81,7 +81,7 @@ @article{dacremaTroublingAnalysisReproducibility2021
   issn = {1094-9224},
   doi = {10.1145/3434185},
   abstract = {The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. To obtain a better understanding of the actual progress, we have compared recent results in the area of neural recommendation approaches based on collaborative filtering against a consistent set of existing simple baselines. The worrying outcome of the analysis of these recent works{\textemdash}all were published at prestigious scientific conferences between 2015 and 2018{\textemdash}is that 11 of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristic or linear models. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.1},
-  keywords = {evaluation,LensKit References,{reproducibility, Recommender systems, deep learning}}
+  keywords = {evaluation,LensKit References,reproducibility Recommender systems deep learning}
 }
 
 @article{deshpandeItembasedTopNRecommendation2004,
@@ -97,7 +97,7 @@ @article{deshpandeItembasedTopNRecommendation2004
   doi = {10.1145/963770.963776},
   urldate = {2015-12-02},
   abstract = {The explosive growth of the world-wide-web and the emergence of e-commerce has led to the development of recommender systems---a personalized information filtering technology used to identify a set of items that will be of interest to a certain user. User-based collaborative filtering is the most successful technology for building recommender systems to date and is extensively used in many commercial recommender systems. Unfortunately, the computational complexity of these methods grows linearly with the number of customers, which in typical commercial applications can be several millions. To address these scalability concerns model-based recommendation techniques have been developed. These techniques analyze the user--item matrix to discover relations between the different items and use these relations to compute the list of recommendations.In this article, we present one such class of model-based recommendation algorithms that first determines the similarities between the various items and then uses them to identify the set of items to be recommended. The key steps in this class of algorithms are (i) the method used to compute the similarity between the items, and (ii) the method used to combine these similarities in order to compute the similarity between a basket of items and a candidate recommender item. Our experimental evaluation on eight real datasets shows that these item-based algorithms are up to two orders of magnitude faster than the traditional user-neighborhood based recommender systems and provide recommendations with comparable or better quality.},
-  keywords = {CAREER,e-commerce,{e-commerce, predicting user behavior, world wide web},Fair Info Access Paper,LensKit References,predicting user behavior,world wide web}
+  keywords = {CAREER,e-commerce,e-commerce predicting user behavior world wide web,Fair Info Access Paper,LensKit References,predicting user behavior,world wide web}
 }
 
 @inproceedings{ekstrandAllCoolKids2018,
@@ -305,7 +305,7 @@ @article{herlockerEmpiricalAnalysisDesign2002
   urldate = {2015-12-02},
   abstract = {Collaborative filtering systems predict a user's interest in new items based on the recommendations of other people with similar interests. Instead of performing content indexing or content analysis, collaborative filtering systems rely entirely on interest ratings from members of a participating community. Since predictions are based on human ratings, collaborative filtering systems have the potential to provide filtering based on complex attributes, such as quality, taste, or aesthetics. Many implementations of collaborative filtering apply some variation of the neighborhood-based prediction algorithm. Many variations of similarity metrics, weighting approaches, combination measures, and rating normalization have appeared in each implementation. For these parameters and others, there is no consensus as to which choice of technique is most appropriate for what situations, nor how significant an effect on accuracy each parameter has. Consequently, every person implementing a collaborative filtering system must make hard design choices with little guidance. This article provides a set of recommendations to guide design of neighborhood-based prediction systems, based on the results of an empirical study. We apply an analysis framework that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component. The three components identified are similarity computation, neighbor selection, and rating combination.},
   langid = {english},
-  keywords = {CAREER,Collaborative filtering,{Data Structures, Cryptology and Information Theory},empirical studies,Fair Info Access Paper,Fall 2017 IR Fairness,information filtering,LensKit References,Management of Computing and Information Systems,preference prediction}
+  keywords = {CAREER,Collaborative filtering,Data Structures Cryptology and Information Theory,empirical studies,Fair Info Access Paper,Fall 2017 IR Fairness,information filtering,LensKit References,Management of Computing and Information Systems,preference prediction}
 }
 
 @inproceedings{huCollaborativeFilteringImplicit2008a,
@@ -456,7 +456,7 @@ @article{ndcg
   address = {{New York, NY, USA}},
   issn = {1094-9224},
   doi = {10.1145/582415.582418},
-  keywords = {CAREER,{Graded relevance judgments, cumulated gain},LensKit References,ndcg}
+  keywords = {CAREER,Graded relevance judgments cumulated gain,LensKit References,ndcg}
 }
 
 @inproceedings{ningSLIMSparseLinear2011,
@@ -533,7 +533,7 @@ @article{pessemierHybridGroupRecommendations2016
   urldate = {2016-03-11},
   abstract = {Recommendation techniques have proven their usefulness as a tool to cope with the information overload problem in many classical domains such as movies, books, and music. Additional challenges for recommender systems emerge in the domain of tourism such as acquiring metadata and feedback, the sparsity of the rating matrix, user constraints, and the fact that traveling is often a group activity. This paper proposes a recommender system that offers personalized recommendations for travel destinations to individuals and groups. These recommendations are based on the users' rating profile, personal interests, and specific demands for their next destination. The recommendation algorithm is a hybrid approach combining a content-based, collaborative filtering, and knowledge-based solution. For groups of users, such as families or friends, individual recommendations are aggregated into group recommendations, with an additional opportunity for users to give feedback on these group recommendations. A group of test users evaluated the recommender system using a prototype web application. The results prove the usefulness of individual and group recommendations and show that users prefer the hybrid algorithm over each individual technique. This paper demonstrates the added value of various recommendation algorithms in terms of different quality aspects, compared to an unpersonalized list of the most-popular destinations.},
   langid = {english},
-  keywords = {CAREER,Collaborative filtering,Computer Communication Networks,Content-based recommender,{Data Structures, Cryptology and Information Theory},Group recommendations,Hybrid,Multimedia Information Systems,Recommender system,Research Using LensKit,Special Purpose and Application-Based Systems,Tourism,Travel,Zotero Import (Mar 30),Zotero Import (Mar 30)/Group Libraries/LensKit}
+  keywords = {CAREER,Collaborative filtering,Computer Communication Networks,Content-based recommender,Data Structures Cryptology and Information Theory,Group recommendations,Hybrid,Multimedia Information Systems,Recommender system,Research Using LensKit,Special Purpose and Application-Based Systems,Tourism,Travel,Zotero Import (Mar 30),Zotero Import (Mar 30)/Group Libraries/LensKit}
 }
 
 @inproceedings{pilaszyFastALSbasedMatrix2010,
@@ -650,7 +650,7 @@ @inproceedings{takacsAlternatingLeastSquares2012
   address = {{New York, NY, USA}},
   doi = {10.1145/2365952.2365972},
   abstract = {Two flavors of the recommendation problem are the explicit and the implicit feedback settings. In the explicit feedback case, users rate items and the user-item preference relationship can be modelled on the basis of the ratings. In the harder but more common implicit feedback case, the system has to infer user preferences from indirect information: presence or absence of events, such as a user viewed an item. One approach for handling implicit feedback is to minimize a ranking objective function instead of the conventional prediction mean squared error. The naive minimization of a ranking objective function is typically expensive. This difficulty is usually overcome by a trade-off: sacrificing the accuracy to some extent for computational efficiency by sampling the objective function. In this paper, we present a computationally effective approach for the direct minimization of a ranking objective function, without sampling. We demonstrate by experiments on the Y!Music and Netflix data sets that the proposed method outperforms other implicit feedback recommenders in many cases in terms of the ErrorRate, ARP and Recall evaluation metrics.},
-  keywords = {{collaborative filtering, alternating least squares, ranking},Exemplars/Introduction,LensKit References}
+  keywords = {collaborative filtering alternating least squares ranking,Exemplars/Introduction,LensKit References}
 }
 
 @inproceedings{takacsApplicationsConjugateGradient2011,
@@ -666,7 +666,7 @@ @inproceedings{takacsApplicationsConjugateGradient2011
   urldate = {2019-07-12},
   abstract = {The need for solving weighted ridge regression (WRR) problems arises in a number of collaborative filtering (CF) algorithms. Often, there is not enough time to calculate the exact solution of the WRR problem, or it is not required. The conjugate gradient (CG) method is a state-of-the-art approach for the approximate solution of WRR problems. In this paper, we investigate some applications of the CG method for new and existing implicit feedback CF models. We demonstrate through experiments on the Netflix dataset that CG can be an efficient tool for training implicit feedback CF models.},
   isbn = {978-1-4503-0683-6},
-  keywords = {collaborative filtering,{collaborative filtering,conjugate gradient method},conjugate gradient method,LensKit References}
+  keywords = {collaborative filtering,collaborative filteringconjugate gradient method,conjugate gradient method,LensKit References}
 }
 
 @inproceedings{tammQualityMetricsRecommender2021,
@@ -681,7 +681,7 @@ @inproceedings{tammQualityMetricsRecommender2021
   doi = {10.1145/3460231.3478848},
   urldate = {2021-10-04},
   abstract = {Offline evaluation is a popular approach to determine the best algorithm in terms of the chosen quality metric. However, if the chosen metric calculates something unexpected, this miscommunication can lead to poor decisions and wrong conclusions. In this paper, we thoroughly investigate quality metrics used for recommender systems evaluation. We look at the practical aspect of implementations found in modern RecSys libraries and at the theoretical aspect of definitions in academic papers. We find that Precision is the only metric universally understood among papers and libraries, while other metrics may have different interpretations. Metrics implemented in different libraries sometimes have the same name but measure different things, which leads to different results given the same input. When defining metrics in an academic paper, authors sometimes omit explicit formulations or give references that do not contain explanations either. In 47\% of cases, we cannot easily know how the metric is defined because the definition is not clear or absent. These findings highlight yet another difficulty in recommender system evaluation and call for a more detailed description of evaluation protocols.},
-  keywords = {LensKit References,{offline evaluation, recommender systems, metrics}}
+  keywords = {LensKit References,offline evaluation recommender systems metrics}
 }
 
 @article{virtanenSciPyFundamentalAlgorithms2020,