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A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews

Abstract

In popular applications such as e-commerce sites and social media, users provide online reviews giving personal opinions about a wide array of items, such as products, services and people. These reviews are usually in the form of free text, and represent a rich source of information about the users’ preferences. Among the information elements that can be extracted from reviews, opinions about particular item aspects (i.e., characteristics, attributes or components) have been shown to be effective for user modeling and personalized recommendation. In this paper, we investigate the aspect-based top-N recommendation problem by separately addressing three tasks, namely identifying references to item aspects in user reviews, classifying the sentiment orientation of the opinions about such aspects in the reviews, and exploiting the extracted aspect opinion information to provide enhanced recommendations. Differently to previous work, we integrate and empirically evaluate several state-of-the-art and novel methods for each of the above tasks. We conduct extensive experiments on standard datasets and several domains, analyzing distinct recommendation quality metrics and characteristics of the datasets, domains and extracted aspects. As a result of our investigation, we not only derive conclusions about which combination of methods is most appropriate according to the above issues, but also provide a number of valuable resources for opinion mining and recommendation purposes, such as domain aspect vocabularies and domain-dependent, aspect-level lexicons.

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Notes

  1. Yelp Challenge dataset, https://www.yelp.com/dataset/challenge.

  2. Amazon reviews dataset, http://jmcauley.ucsd.edu/data/amazon.

  3. Aspect opinion resources, http://ir.ii.uam.es/aspects.

  4. AllMusic record reviews, https://www.allmusic.com.

  5. GameSpot Video Games reviews and news https://www.gamespot.com.

  6. WordNet lexical database, https://wordnet.princeton.edu.

  7. Penn Treebank, http://web.mit.edu/6.863/www/PennTreebankTags.html.

  8. Double negations of adjectives in sentences are also recognized by our method.

  9. The identification of nouns includes compound nouns, by means of the compound, nn and nmod relations.

  10. Thesaurus.com - synonyms and antonyms, http://www.thesaurus.com.

  11. TripAdvisor travel and restaurant review site, https://www.tripadvisor.com.

  12. British National Corpus, http://www.natcorp.ox.ac.uk.

  13. RankSys recommender systems evaluation framework, http://ranksys.org.

  14. RiVal recommender system evaluation toolkit, http://rival.recommenders.net.

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Acknowledgements

This work was supported by the Spanish Ministry of Economy, Industry and Competitiveness (TIN2016-80630-P). The authors thank the reviewers for their thoughtful comments and suggestions.

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A Appendix

A Appendix

For the sake of reproducibility, in Table 9 we present the optimal parameter values found for the recommendation methods presented in Sect. 6, and, specifically, for the results reported in Tables 5 and 7.

These parameters were obtained by running all the possible method combinations, and selecting the best performing ones according to P@5. In particular, a grid search was conducted based on the following values of the parameters:

  • Number of neighbors (rec column for ub, cbib, and cbub): 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.

  • Number of latent factors (rec column for mf): 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100.

  • Threshold to select terms (met column when asp is sab): 0.1, 0.05, 0.03, 0.01, 0.005, 0.003, 0.001.

  • Top terms (met column when asp is dp or dpp): 10, 20, 50, 100, 200, 500.

  • Number of latent topics (met column when asp is lda): 5, 10, 20, 50, 100.

  • Maximum number of words from the corpus (rec column for hft): 5 K, 50 K, 500 K. The regularizers for the latent topic (0, 0.1, 0.5) and MF (0.1, 0.5, 1) as well as the number of latent factors/topics (5, 10) were also tested but no important differences were observed, as in the original paper; hence, 0, 0.1 and 5 were used for these parameters in every dataset.

Note that the non-personalized techniques such as rnd and ipop do not use any parameter (denoted as \(-\) in the table); furthermore, pure collaborative filtering algorithms (ib, ub, mf) do not need any parameter regarding the aspect extraction method because they do not exploit aspect opinion information. It should also be noted that the cb pure content-based method and the voc vocabulary-based aspect extraction method do not have parameters either. Additionally, as a representative example, in Table 10 we show the extracted aspects by the Double Propagation and SABRE methods using top 20 terms and 0.01 threshold, respectively.

Table 9 Parameter values of the recommenders (rec column) and aspect extraction methods (asp column) whose results are reported in Tables 5 and 7
Table 10 Extracted aspects with Double Propagation and SABRE

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Hernández-Rubio, M., Cantador, I. & Bellogín, A. A comparative analysis of recommender systems based on item aspect opinions extracted from user reviews. User Model User-Adap Inter 29, 381–441 (2019). https://doi.org/10.1007/s11257-018-9214-9

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Keywords

  • Recommender systems
  • Aspect-based recommendation
  • Sentiment analysis
  • Opinion mining
  • Aspect extraction
  • User reviews