Name
Abstract | ||
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Over the last years, we have been witnessing the advent of more and more precise and powerful recommendation algorithms and techniques able to effectively assess users' tastes and predict information that would probably be of interest for them. Most of these approaches rely on the collaborative paradigm (often exploiting machine learning techniques) and do not take into account the huge amount of knowledge, both structured and non-structured ones, describing the domain of interest of the recommendation engine. Although very effective in in predicting relevant items, collaborative approaches miss some very interesting features that go beyond the accuracy of results and move into the direction of providing novel and diverse results as well as generating an explanation for the recommended items or support interactive and conversational recommendation processes.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3357384.3358805 | Proceedings of the 28th ACM International Conference on Information and Knowledge Management |
Keywords | Field | DocType |
conversational agents, knowledge base, knowledge graph, knowledge-aware, linked data, natural language processing | Recommender system,Information retrieval,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-4503-6976-3 | 3 | 0.37 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vito Walter Anelli | 1 | 91 | 18.45 |
| Tommaso Di Noia | 2 | 1857 | 152.07 |