An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Abstract
:1. Introduction
2. Materials and Methods
2.1. Neighborhood-Based Collaborative Filtering
2.2. Item Similarity
2.2.1. Textual Representation
2.2.2. Content Representation Based on Controlled Vocabularies
2.2.3. Ontology-Based Similarity Functions
- d: The maximum number of steps from any entity to .
- m: The maximum number of steps between any pair of entities.
- : The number of steps from entity t to .
- : The number of steps from entity t to entity u.
- : The least common subsumer of t and u (i.e., their deepest common ancestor).
- : The information content (IC) of entity t as proposed by Resnik et al. [46]. is a measure of the informativeness of an entity in a hierarchy obtained from statistics gathered from a corpus. In our scenario, the corpus is a collection of items, each of which is represented by a set of features associated with entities in an ontology. Thus, the of an entity t is defined as , where is the probability of t in the corpus. For instance, assume the corpus of items to be a set of TV episodes whose features are themes from a theme ontology. In addition, assume the following path of theme entities in an is-a chain: “wedding ceremony” → “ceremony” → “event” → . The probability of an entity t is the ratio between its number of occurrences and the total number of entity occurrences in the corpus M. Each occurrence of the theme “wedding ceremony” increases the counting up the hierarchy until is reached. Therefore, and . The IC scores agree with the information theoretical principle that events having low probability are highly informative and vice versa.
2.3. A Literary Theme Ontology with Application to Star Trek Television Series Episodes
2.3.1. The Star Trek Television Series
2.3.2. The Literary Theme Ontology
- The Human Condition:
- Themes pertaining to the inner and outer experiences of individuals be they about private life or pair and group interactions with others around them.
- Society:
- Themes pertaining to individuals involved in persistent social interaction, or a large social group sharing the same geographical or social territory, typically subject to the same political authority and dominant cultural expectations. These are themes about the interactions and patterns of relationships within or between different societies.
- The Pursuit of Knowledge:
- Themes pertaining to the expression of a view about how the world of nature operates, and how humans fit in relation to it. Put another way, these are themes about scientific, religious, philosophical, artistic, and humanist views on the nature of reality.
- Alternate Reality:
- Themes related to subject matter falling outside of reality as it is presently understood. This includes, but is not limited to, science fiction, fantasy, superhero fiction, science fantasy, horror, utopian and dystopian fiction, supernatural fiction as well as combinations thereof.
2.3.3. A Thematically Annotated Star Trek Episode Dataset
2.4. Episode Transcripts
2.5. User Preferences
3. Experimental Validation
3.1. Experimental Setup
- CBF recommenders using transcripts:
- These methods use the data and preprocessing procedure described in Section 2.3. TFIDF is implemented using Equations (3) and (4), and LSI is implemented by performing SVD, as described in Section 2.2.1, on the document-term matrix obtained from the data. The number of latent factors was varied from 10 to 100 in increments of 10. Both approaches are implemented using the Gensim (Gensim: Topic modelling for humans (2019). URL: https://radimrehurek.com/gensim/. (Online; accessed 30 June 2019)) text processing library [71].
- KBF recommenders using themes:
- The three methods described in Section 2.2.2 applied to the thematically annotated representation of the episodes described in Section 2.3.3. JACCARD and DICE were implemented using the Equation (5) formulae as item similarity functions, while COSINE_IDF was implemented using Equation (6).
- OBF recommenders using themes and the ontology:
- This category comprises the methods introduced in this work, which are described in detail in Section 2.2.3. These methods make use of the thematically annotated Star Trek episodes described in Section 2.3.3, and of the LTO themes as presented in Section 2.3.2. Each of the six variants is named after the abbreviation for their assocaited item similarity function: , , , , , and .
- CF recommenders and baselines:
- In this group, we tested a set of classical RSs based purely on user ratings. These methods can be grouped into KNN [14] and matrix factorization approaches [15,66,67,68,69,70]. In addition, we included five popular baseline methods: (1) User Item Baseline, which produces rating predictions using the baselines described in Section 2.1; (2) Item Average Baseline, which uses as predictions the mean rating of each item; (3) User Average Baseline same as before, but averaging by user; (4) Global Average Baseline which always predicts the average rating of the dataset; and (5) Random Baseline, which produces random ratings distributed uniformly.
3.2. Results
3.3. Results Discussion
4. Discussion
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
BFO | Basic Formal Ontology |
CF | collaborative filtering (general approach for recommender systems) |
CBF | content-based filtering (general approach for recommender systems) |
DF | document frequency |
FM | factor model (general approach for CF recommender systems) |
IC | information content |
IDF | inverse document frequency |
IKNN | item K-nearest neighbors (method for recommender systems) |
IMDb | The Internet Movie Database |
JCH | Jiang and Conrath measure [73] (entity similarity function in an ontology hierarchy) |
KBF | knowledge-based filtering (general approach for recommender systems) |
LCH | Leacock and Chodorow measure [47] (entity similarity function in an ontology hierarchy) |
LCS | least common subsumer between two entities in an ontology |
LIN | Lin’s measure [49] |
LSI | latent semantic indexing (method for text representation) |
LTO | Literary Theme Ontology |
MF | matrix factorization |
NLTK | Natural Language Toolkit |
OBF | ontology-based filtering (general approach for recommender systems) |
RES | Resnik’s measure [46] (entity similarity function in an ontology hierarchy) |
RMSE | root-mean square error |
RS | recommender system |
SVD | singular value decomposition |
s.d. | standard deviation |
TAS | Star Trek: The Animated Series (series of Star Trek TV episodes) |
TF | term frequency |
TFIDF | term frequency–inverse document frequency (method for text representation) |
TNG | Star Trek: The Next, Generation (series of Star Trek TV episodes) |
TOS | Star Trek: The Original Series (series of Star Trek TV episodes) |
WUP | Wu and Palmer’s measure [48] (entity similarity function in an ontology hierarchy) |
Appendix A
Literary Theme | Domain | Level | Comment |
---|---|---|---|
avarice | central | Arridor and Kol exploit a Bronze Age people for economic gain. | |
exploitation of sentient beings | central | Arridor and Kol exploit a Bronze Age people for economic gain. | |
fraud | central | Arridor and Kol fraudulently claim to be the Holy Sages prophesied in Takarian sacred scripture. | |
primitive point of view | central | The viewer is made to see the world through the eyes of a Bronze Age people. | |
religion as a control mechanism | central | Arridor and Kol use religion as a means of exploiting a technologically lesser advanced people. | |
science as magic to the primitive | central | Arridor and Kol use advanced technology to trick a Bronze Age people into thinking them gods. | |
the ethics of interfering in less advanced societies | central | Captain Janeway argued she had the authority to depose Arridor and Kol from their seat of power on the Takarian homeworld because the Federation was responsible for the cultural contamination caused by their arrival. | |
the fulfillment of prophesy | central | Arridor and Kol fraudulent claim to be the Holy Sages prophesied in Takarian sacred scripture. | |
the lust for gold | central | Arridor and Kol exploit a Bronze Age people for economic gain. | |
casuistry in interpretation of scripture | peripheral | Arridor and Kol advocated a nonliteral interpretation of the passage in Takarian sacred scripture condemning them to being burned at the stake. | |
wormhole | peripheral | Arridor and Kol travel through a wormhole to reach the Takarian homeworld. | |
matter replicator | peripheral | Arridor and Kol proliferated matter replicator technology on the Takarian homeworld. |
Appendix B
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Series Title | Short Name | Original Release | No. of Seasons | No. of Episodes |
---|---|---|---|---|
Star Trek: The Original Series | TOS | 1966–1969 | 3 | 79 |
Star Trek: The Animated Series | TAS | 1973–1974 | 2 | 22 |
Star Trek: The Next, Generation | TNG | 1987–1994 | 7 | 178 |
Star Trek: Deep Space Nine | DS9 | 1993–1999 | 7 | 177 |
Star Trek: Voyager | Voyager | 1995–2001 | 7 | 172 |
Star Trek: Enterprise | Enterprise | 2001–2005 | 4 | 99 |
Star Trek: Discovery | Discovery | 2017–present | 2 | 29 |
Star Trek: Shorts | Shorts | 2018–present | 1 | 4 |
Domain Root Theme | Domain Color-Code | Theme Count | Leaf Theme Count | Tree Height |
---|---|---|---|---|
the human condition | 892 | 835 | 6 | |
society | 387 | 362 | 4 | |
the pursuit of knowledge | 329 | 308 | 4 | |
alternate reality | 521 | 484 | 4 |
Series Short Name | No. of Episodes | Mean Number of Central Themes per Episode ± S.D. | Mean Number of Peripheral Themes per Episode ± S.D. |
---|---|---|---|
TOS | 80 | 12.42 ± 4.31 | 20.05 ± 6.23 |
TAS | 22 | 6.77 ± 2.58 | 3.41 ± 2.28 |
TNG | 178 | 11.64 ± 4.38 | 14.88 ± 5.60 |
Voyager | 172 | 9.20 ± 2.99 | 7.63 ± 3.38 |
# | Type | Method Description | Warm System Scenario | Cold Start Scenario | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
RMSE ± s.d. | 1 | 2 | 3 | 4 | 5 | 6 | RMSE ± s.d. | 1 | 2 | 3 | 4 | 5 | 6 | |||
1 | CBF | IKNN-LSI-40, k = 40 [this paper] | = | * | * | * | * | = | * | * | * | |||||
2 | OBF | IKNN-RES, p = 2, k = 50 [this paper] | * | = | * | * | * | = | * | |||||||
3 | OBF | IKNN-LCH, p = 4, k = 80 [this paper] | * | = | * | * | * | = | * | |||||||
4 | KBF | IKNN-DICE, k = 70 [this paper] | = | * | = | * | ||||||||||
5 | CF | IKNN, k = 40 [14] | * | * | * | = | * | = | * | |||||||
6 | CF | Sig. Item Asymm. FM, f = 10 [66] | * | * | * | * | * | = | * | * | * | * | * | = | ||
CF | Sig. Comb. Asymm. FM, f = 10 [66] | * | * | * | * | * | * | * | * | * | * | |||||
CF | Sig. User Asymm. FM, f = 10 [66] | * | * | * | * | * | * | * | * | * | * | |||||
CF | User Item Baseline [14] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | User KNN, k = 80 [14] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | Biased MF, f = 10 [68] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | SVD Plus Plus, f = 10 [69] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | Slope One [70] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | Item Average Baseline | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | User Average Baseline | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | MF, f = 10 [15] | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | Global Average Baseline | * | * | * | * | * | * | * | * | * | * | * | ||||
CF | Factor Wise MF, f = 10 [67] | * | * | * | * | * | * | * | * | * | * | * | ||||
Random Baseline | * | * | * | * | * | * | * | * | * | * | * | * |
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Sheridan, P.; Onsjö, M.; Becerra, C.; Jimenez, S.; Dueñas, G. An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet 2019, 11, 182. https://doi.org/10.3390/fi11090182
Sheridan P, Onsjö M, Becerra C, Jimenez S, Dueñas G. An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet. 2019; 11(9):182. https://doi.org/10.3390/fi11090182
Chicago/Turabian StyleSheridan, Paul, Mikael Onsjö, Claudia Becerra, Sergio Jimenez, and George Dueñas. 2019. "An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise" Future Internet 11, no. 9: 182. https://doi.org/10.3390/fi11090182
APA StyleSheridan, P., Onsjö, M., Becerra, C., Jimenez, S., & Dueñas, G. (2019). An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise. Future Internet, 11(9), 182. https://doi.org/10.3390/fi11090182