Review Comment:
The survey gives an overview of the field of knowledge-aware new recommender systems which differentiates itself from other surveys on news recommender systems due to the focus on knowledge-based solutions. It proposes a simple (maybe too simple) taxonomy by means of which the various works from the literature are presented. Next to the algorithms, also evaluation methods and future research directions are investigated. The paper is in general well-written and relatively easy to follow. Below, comments are given which could help the authors improve their paper.
It would be nice to present the various algorithms based on their commonalities and keep the presentation at a more abstract level as sometimes the level of details given is too specific but not enough for a full comprehension. Also, a glossary of terms and/or acronyms could be useful given the extensive technical jargon used (similar to Table 4).
For the Hermes-related papers the Data Source is always Reuters (the same 100 news items) and not the Hermes News Portal or Unknown as claimed in Tables 6 and 7 (which are also inconsistent with one another with respect to this aspect). This is valid for CF-IDF, SF-IDF, SF-IDF+, Bing-SF-IDF, Bing-SF-IDF+, CF-IDF+, Bing-CF-IDF+, Bing-CSF-IDF+, and Bing-SS.
The authors touch on information networks as representations of knowledge graphs (outside the news domain) but there are works not referred to which deal with feature selection across various network paths and weighting these by considering node’s centrality:
Bart van Rossum, Flavius Frasincar: Augmenting LOD-Based Recommender Systems Using Graph Centrality Measures. ICWE 2019: 19-31
Thomas Wever, Flavius Frasincar: A Linked Open Data Schema-Driven Approach for Top-N Recommendations. SAC 2017: 656-663
Tommaso Di Noia, Vito Claudio Ostuni, Paolo Tomeo, Eugenio Di Sciascio: SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data. ACM Trans. Intell. Syst. Technol. 8(1): 9:1-9:34 (2016)
Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi: Top-N Recommendations from Implicit Feedback Leveraging Linked Open Data. RecSys 2013: 85-92
In the surveyed approaches it is not clear if there are knowledge-based solutions for sequential recommender systems for news. In the open issues, Hermes is able to update the knowledge base based on information from news:
Flavius Frasincar, Jethro Borsje, and Frederik Hogenboom: Personalizing News Services Using Semantic Web Technologies. E-Business Applications for Product Development and Competitive Growth: Emerging Technologies, In Lee (Ed.), Chapter 13, pages 261-289, IGI Global (2011)
and tOWL is able to store dynamic information in a knowledge base also coming from news:
Viorel Milea, Flavius Frasincar, Uzay Kaymak: tOWL: A Temporal Web Ontology Language. IEEE Trans. Syst. Man Cybern. Part B 42(1): 268-281 (2012)
Other comments:
-throughout the manuscript: do not use indentation before “where” when you explain on a new line what the terms of the previously mentioned equation mean
-page 1: in title “on” instead of “On”
-page 10: “models use” instead of “model uses”
-page 13: “two representations” instead of “two profiles”
-page 14: text goes outside borders
-page 20: “artificial intelligence.” instead of “artificial intelligence .” (delete extra space)
-page 21: “from the least common subsumer to” instead of “lowest to”
-page 25: “context (e)” instead of “context(e)” (insert space)
-page 25: “Eqs. (50)” instead of “Eqs.(50)” (insert space)
-page 26: e(i) missing in Eq. (57)
-page 29: explain the symbol with a dot in Eq. (68)
-page 28: “as well as” instead of “as well and”
-page 35: for the row starting with Bing-SF-IDF [60] following text needs to move one column to the right “Reuters, English, …” and end with “N/A”
-page 42: “comprise many” instead of “comprise of many”
-page 44: “user interests” instead of “user interest”
-page 47: “WordNet” instead of “Wordnet”
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