Crowd-Based Personalized Natural Language Explanations for Recommendations

Chang, Shuo; Harper, F. Maxwell; Terveen, Loren

doi:10.1145/2959100.2959153

Cited by 88 publications

(60 citation statements)

References 23 publications

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“…By training over large-scale user reviews, the model can generate reasonable review sentences as explanations, as shown in Figure 2.8. Inspired by how people explain word-of-mouth recommendations, Chang et al (2016) proposed a process to combine crowdsourcing and computation to generate personalized natural language explanations. The authors also evaluated the generated explanations in terms of efficiency, effectiveness, trust, and satisfaction.…”

Section: Sentence Explanationmentioning

confidence: 99%

“…(b) Example natural language explanations for the movie "Gravity". Depending on the model of a user's interest, the system selects one crowd-sourced explanation for the user (Chang et al, 2016). Gao et al (2019) developed a Deep Explicit Attentive Multi-view Learning Model (DEAML) for explainable recommendation, which aims to mitigate the trade-off between accuracy and explainability by developing explainable deep models.…”

Section: Deep Learning For Explainable Recommendationmentioning

confidence: 99%

“…The model enhances the accuracy of the recommendation task, and generates linguistic explanations that are fluent, useful, and highly personalized. Chang et al (2016) proposed another approach to generating natural language explanations, which is based on human users and crowdsourcing. The authors argued that algorithm generated explanations can be overly simplistic and unconvincing, while humans can overcome these limitations.…”

Section: Deep Learning For Explainable Recommendationmentioning

confidence: 99%

See 2 more Smart Citations

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

568

241

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Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -especially model-based methods -have been proposed and applied in real-world systems.In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation, including user study approaches in the early years and more recent model-based approaches. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research: one dimension is the information source (or display style) of the explanations, and the other dimension is the algorithmic mechanism to generate explainable recommendations. 3) We summarize how explainable recommendation applies to different recommendation tasks, such as product recommendation, social recommendation, and POI recommendation.We also devote a section to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.Explainable recommendation refers to personalized recommendation algorithms that address the problem of why -they not only provide users or system designers with recommendation results, but also explanations to clarify why such items are recommended. In this way, it helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and user satisfaction of the recommendation systems. It also facilitates system designers to diagnose, debug, and refine the recommendation algorithm.To highlight the position of explainable recommendation in the recommender system research area, we classify personalized recommendation with a broad conceptual taxonomy. Specifically, personalized recommendation research can be classified into the 5W problemswhen, where, who, what, and why, corresponding to time-aware recommendation (when), location-based recommendation (where), social recommendation (who), application-aware re...

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Section: Sentence Explanationmentioning

confidence: 99%

Section: Deep Learning For Explainable Recommendationmentioning

confidence: 99%

Section: Deep Learning For Explainable Recommendationmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Recommendation: A Survey and New Perspectives

Zhang

Chen

2020

FNT in Information Retrieval

568

241

View full text Add to dashboard Cite

show abstract

“…Hence, such evaluation techniques are called offline experiments. Such experiments do not allow to capture the factors influencing user satisfaction, or what happens with the quality or perception of the predictions over time [38], [39], [40], [41], or aspects of user interaction with the recommender.…”

Section: Recommender Systemsmentioning

confidence: 99%

Does Reviewer Recommendation Help Developers?

Kovalenko

Tintarev

Pasynkov³

et al. 2020

IIEEE Trans. Software Eng.

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Abstract-Selecting reviewers for code changes is a critical step for an efficient code review process. Recent studies propose automated reviewer recommendation algorithms to support developers in this task. However, the evaluation of recommendation algorithms, when done apart from their target systems and users (i.e., code review tools and change authors), leaves out important aspects: perception of recommendations, influence of recommendations on human choices, and their effect on user experience. This study is the first to evaluate a reviewer recommender in vivo. We compare historical reviewers and recommendations for over 21,000 code reviews performed with a deployed recommender in a company environment and set out to measure the influence of recommendations on users' choices, along with other performance metrics. Having found no evidence of influence, we turn to the users of the recommender. Through interviews and a survey we find that, though perceived as relevant, reviewer recommendations rarely provide additional value for the respondents. We confirm this finding with a larger study at another company. The confirmation of this finding brings up a case for more user-centric approaches to designing and evaluating the recommenders. Finally, we investigate information needs of developers during reviewer selection and discuss promising directions for the next generation of reviewer recommendation tools. Preprint: https://doi.org/10.5281/zenodo.1404814

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“…Recently, Chang et al [1] described a crowdsourcing-based framework for generating natural language explanations which relies on specific human-generated annotations, whereas our system harnesses the ongoing work of Wikipedia editors, and automatically assigns labels to explain a given recommendation. Moreover, the use of a rich set of Wikipedia articles and categories as features helps to highlight serendipitous aspects of recommended items which are otherwise difficult to discover.…”

Section: Introductionmentioning

confidence: 99%

Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors

Qureshi

Greene

2017

Machine Learning and Knowledge Discovery in Databases

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Abstract. We present an explainable recommendation system for novels and authors, called Lit@EVE, which is based on Wikipedia concept vectors. In this system, each novel or author is treated as a concept whose definition is extracted as a concept vector through the application of an explainable word embedding technique called EVE. Each dimension of the concept vector is labelled as either a Wikipedia article or a Wikipedia category name, making the vector representation readily interpretable. In order to recommend items, the Lit@EVE system uses these vectors to compute similarity scores between a target novel or author and all other candidate items. Finally, the system generates an ordered list of suggested items by showing the most informative features as human-readable labels, thereby making the recommendation explainable.

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Crowd-Based Personalized Natural Language Explanations for Recommendations

Cited by 88 publications

References 23 publications

Explainable Recommendation: A Survey and New Perspectives

Explainable Recommendation: A Survey and New Perspectives

Does Reviewer Recommendation Help Developers?

Lit@EVE: Explainable Recommendation Based on Wikipedia Concept Vectors

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