Name
Abstract | ||
|---|---|---|
Recent research focuses beyond recommendation accuracy, towards human factors that influence the acceptance of recommendations, such as user satisfaction, trust, transparency and sense of control. We present a generic interactive recommender framework that can add interaction functionalities to non-interactive recommender systems. We take advantage of dialogue systems to interact with the user and we design a middleware layer to provide the interaction functions, such as providing explanations for the recommendations, managing users' preferences learnt from dialogue, preference elicitation and refining recommendations based on learnt preferences.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3298689.3346966 | Proceedings of the 13th ACM Conference on Recommender Systems |
Keywords | Field | DocType |
interaction mechanism, pref. elicitation, recommender system | World Wide Web,Computer science,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-6243-6 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oznur Kirmemis Alkan | 1 | 28 | 4.45 |
| Massimiliano Mattetti | 2 | 5 | 3.19 |
| Elizabeth Daly | 3 | 8 | 6.43 |
| Adi Botea | 4 | 698 | 62.87 |
| Inge Vejsbjerg | 5 | 1 | 1.71 |