In this paper, we explore the use of ILP thoroughly in generating explainable, negative, group and context-aware recommendation. ILP provides recommendation rules in if-then logical format that allows us to form a clear and concise explanation to accompany the suggested items. It can indirectly derive negative rules which tell us not to recommend certain products to users. It also emphasizes the use of universal representations which enables us to suggest the items to a group of users who share the same interest. Additionally, ILP requires no re-training if new contexts (e.g., location, time and mood) are added to the system to generate context-aware recommendations (CARS), only predicates and settings are simply specified. In this paper, we also propose the explainability evaluation in terms of transparency by comparing the items/features appearing in the explanation with the features presented in the user's review. The negative, group and dynamic recommendations can be evaluated using the standard measurement

Fukui, Ken-ichi

Numao, Masayuki

Sopchoke, Sirawit

English

White Rose Research Online

This is a repository copy of ILP Recommender System: Explainable and More.White Rose Research Online URL for this paper:http://eprints.whiterose.ac.uk/157687/Conference or Workshop Item:Sopchoke, Sirawit, Fukui, Ken-ichi and Numao, Masayuki (2019) ILP Recommender System: Explainable and More. In: The 29th ILP conference, 03-05 Sep 2019, Plovdiv, Bulgaria. eprints@whiterose.ac.ukhttps://eprints.whiterose.ac.uk/Reuse Items deposited in White Rose Research Online are protected by copyright, with all rights reserved unless indicated otherwise. They may be downloaded and/or printed for private study, or other acts as permitted by national copyright laws. The publisher or other rights holders may allow further reproduction and re-use of the full text version. This is indicated by the licence information on the White Rose Research Online record for the item. Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. The 29th ILP conference, 3–5 Sep 2019, Plovdiv, Bulgaria.ILP Recommender System:Explainable and More⋆Sirawit Sopchoke1, Ken-ichi Fukui2, and Masayuki Numao21 Graduate School of Information Science and Technology, Osaka University, Japan2 The Institute of Scientific and Industrial Research (ISIR), Osaka University, JapanAbstract. In this paper, we explore the use of ILP thoroughly in gener-ating explainable, negative, group and context-aware recommendation.ILP provides recommendation rules in if-then logical format that allowsus to form a clear and concise explanation to accompany the suggesteditems. It can indirectly derive negative rules which tell us not to recom-mend certain products to users. It also emphasizes the use of universalrepresentations which enables us to suggest the items to a group of userswho share the same interest. Additionally, ILP requires no re-trainingif new contexts (e.g., location, time and mood) are added to the sys-tem to generate context-aware recommendations (CARS), only predi-cates and settings are simply specified. In this paper, we also proposethe explainability evaluation in terms of transparency by comparing theitems/features appearing in the explanation with the features presentedin the user’s review. The negative, group and dynamic recommendationscan be evaluated using the standard measurement.1 IntroductionIn recent years, an interest in recommender system (RS) has dramatically in-creased. A variety of innovative RS e.g. explainable RS, group RS and CARSwere developed. Explainable RS provides explanations to make the users awarewhy the items are recommended to them and helps to improve the effective-ness, efficiency, persuasiveness, and user satisfaction of RS. However, most ofthe explanation forms e.g. textual sentence, tag cloud, visual image seem tobe complicated and need an extra effort to comprehend. Group RS providesrecommendations to a group of users. A group recommender is useful for do-mains where several people participate in a single activity. Most of the groupRS developed use aggregation strategies. However, it is difficult to adapt to thegroup as a whole based on information about individual users’ likes and dislikes.Recommending to groups is clearly more complicated than recommending to in-dividuals. In CARS, user preferences may change over context. CARS generatesmore relevant recommendations by adapting them to a specific contextual situa-tion of a user. However, there are some concerns regarding how the informationrepresenting the context is obtained and how the contextual user preference is⋆ Supported by the JSPS Core-to-Core Program and the Brain Circulation project.12 The 29th ILP conference, 3–5 Sep 2019, Plovdiv, Bulgaria.elicited. These three different purposes of RS inspire us to extend our proposedRS into a new and challenging RS. Another aim of our system is to incorporatethe negative recommendations which refer to the items that the system will notsuggest to the user. Hence, we decide to explore ILP thoroughly so as to makeour RS to provide or not to provide the recommendation in the right context tothe right person or the group with a readable explanation.To evaluate the quality of our recommendation, the standard measurementcan be used. However, to evaluate the explainability in terms of transparency, wepropose a less complicated method compared to the existing methods since ourdesigned explanation is in a simple form with a clear and concise information.2 Related WorkThe success of ILP in RS has been shown in a number of researches. For example,Kouki et al. [1] showed that relational learning could be used to develop a generaland extensible hybrid RS framework. This framework provided a mechanism toextend the system by incorporating and reasoning over unspecified types andsimilarity measures of additional information collected from several sources. Asa result, we were drawn into relational learning to provide potentially a solutionto construct explanation for recommendations.Explanations in RS can serve multiple purposes [5], one of which is trans-parency. An explanation provides clarity as to how a recommendation is pickedfor a user and it can be helpful especially when the system shows multiple rec-ommendations. Most explanations [2] are presented in natural language whichcould possibly lead to a misunderstanding due to their complexity and lack ofclarity. It is sometimes difficult to use language in a precise and unambiguousway without making the explanation wordy and difficult to read. Group recom-mendation has attracted significant research efforts since it benefits a group ofusers. PolyLens is the first RS to recommend movies to groups of users. Ag-gregation is a main strategy used in most researches in group RS with a smallvariation to suit each purpose [3]. Recommending to groups is clearly more com-plicated than recommending to individuals since each user may have differentpreferences. CARS is RS which incorporates contextual information. The im-portance of using context data in the RS can be found in [3]. The challengesin CARS research includes how to actually capture and exploit context. To ourknowledge, research in negative recommendation has not yet been available.Most of the approaches to evaluate explainable recommendations are throughonline experiments by analyzing the behaviors of real users [2]. The offline ex-periment of any form of explanation still, however, lacks a standard evaluation.3 ILP Recommender SystemIn this research, our goal is to combine explainable, negative, group and context-aware recommendation into a single RS. In 2018, we succeeded in constructingthe cross-domain recommendation rule using ILP [4]. ILP is preferred over otherA late breaking abstract published under a CC BY licence. 3machine learning approaches because of its ease of comprehensibility, intelli-gibility, and ability to include additional information in the learning problem.Moreover, ILP provides us with an expressive first-order logical rule. We con-ducted an experiment over the dataset containing user’s preferences and itemattributes. The dataset was obtained from “Amazon product data” providedby UCSD3. Each recommendation is conceptually illustrated with the followingexamples:Explainable recommendation The explanation that will accompany the itemsuggested by the recommendation rule (1) is “If User1 and User2 like the samemovie genre thriller, then User2 will like the same music as User1” with a prob-ability of 0.69523. The rule comprises a condition part and a conclusion part.likeMusic(User2,Music) :- likeMovie(User2,Movie1),likeMovie(User1,Movie2),movieGenre(Movie1, thriller),movieGenre(Movie2, thriller), likeMusic(User1,Music). 0.69523(1)Group recommendation The recommendation rule (2) allows us to suggestthe music by Linkin Park to the group of users whose preference is Stan Lee’sbook. In ILP, universal quantification is assumed over all literals which meansthe rule can be used to recommend to the group of users who share the samepreferences.likeMusic(User,Music) :- likeBook(User,Book),bookAuthor(Book, stanlee),musicArtist(Music, linkinpark). 0.63751(2)Context-aware recommendation In our preliminary experiment, the con-textual information was not incorporated at the time. The recommendation rule(3) is not from our experiment but it is what we expect from our framework,the probability is therefore not available. According to (3), music2 is recom-mended to the user since he/she is sleeping and his/her preference is music1.The sleeping context is considered in suggesting the music.likeMusic(User,music2) :- likeMusic(User,music1),userActivity(User, sleeping). P robability P(3)Negative recommendation The negative recommendation was not includedin our preliminary experiment either. However, we believe that we can trans-form a generated rule into an equivalent negative form of which the conclusionpart is negative. The expected rule is shown in (4). The music1 will not berecommended to the user because the user is feeling sad at that moment.not(likeMusic(User,music1)) :- userMood(User, sad). P robability P (4)3 http://jmcauley.ucsd.edu/data/amazon/4 The 29th ILP conference, 3–5 Sep 2019, Plovdiv, Bulgaria.4 Explainability EvaluationThe standard measurement can be used to evaluate the quality of the recom-mendation. However, we propose the method for transparency evaluation forexplainability based on historical data (offline evaluation). The method is todetermine whether the items appearing in the explanation and the items ap-pearing in the user review texts belong to the same domains (i.e. the generatedexplainable rule can be considered as the generated user review). In performingthe evaluation, we first randomly selected 100 users. For each user, we had top10 items which were recommended by our generated rules from our proposedframework. We then selected only the recommended items which were founduseful to the users and investigated the review texts that the users wrote forthe (recommended) items in the repository. If there was a piece of free text inthe particular review that corresponded to the generated rules by our RS e.g.“movie” term was found in the user review text on a music recommended bythe generated movie-music rule, the explanation of the rules was transparentand useful to the user. Our evaluation results showed 81% relevance for therecommendations from movie-music rules and 73% from book-music rules.5 Conclusion and Future WorkThe ability of ILP in using an expressive representation language allows us tocomplete our RS in providing a readable explanation; a group recommendationbased on relations between users with different preferences without complicatedaggregation; a recommendation with a specific situation of the user by simplyadding the contextual knowledge. The negative recommendation can be accom-plished by negating the suggested item. The ongoing work includes experiment-ing on context-aware and negative recommendation, performing both offline andonline evaluation on explainability, and combining deep learning (embedding-based RS model) to enhance the ability of our framework.References1. Kouki, P., Fakhraei, S., Foulds, J., Eirinaki, M., Getoor, L.: Hyper: A flexible andextensible probabilistic framework for hybrid recommender systems. In: 9th ACMConference on Recommender Systems. pp. 99–106. ACM, New York, NY (2015)2. Nunes, I., Jannach, D.: A systematic review and taxonomy of explanations in deci-sion support and recommender systems. User Modeling and User-Adapted Interac-tion 27(3), 393–444 (2017)3. Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. SpringerUS, Boston, MA, 2 edn. (2015)4. Sopchoke, S., Fukui, K., Numao, M.: Explainable cross-domain recommendationsthrough relational learning. In: The Thirty-Second AAAI Conference on ArtificialIntelligence (AAAI-18). pp. 8159–8160 (2018)5. Tintarev, N., Masthoff, J.: Explaining Recommendations: Design and Evaluation,pp. 353–382. Springer US, Boston, MA (2015)

ILP Recommender System: Explainable and More

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ILP Recommender System: Explainable and More

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