Searching and Recommending TV series with SQL
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This document describes Series-O-Rama, a system that allows users to search for and get recommendations on TV series using SQL. It mines subtitles to extract terms for each series. These terms are indexed and weighted using TF-IDF to model each series as a vector. Series similarity is calculated based on shared terms. Queries can retrieve matching series based on term weights and series can be recommended based on a user's interests. The system provides search, browsing and recommendation capabilities through its GUI and uses a database to store the subtitle data and indexes.
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Searching and Recommending TV series with SQL
- 1. Series-O-RamaSeries-O-Rama Search & Recommend TV series with SQLSearch & Recommend TV series with SQL http://bit.ly/series-o-ramahttp://bit.ly/series-o-rama Guillaume Cabanac guillaume.cabanac@univ-tlse3.fr
- 2. Toulouse: A Picture is Worth a Thousand Words Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac 2 1 2 3 4 Capbreton 3h ride Toulouse population: 437 000 students: 97 000 Ax-les-Thermes 1h40 ride Collioure 2h30 ride
- 3. en.wikipedia.org Telly Addicts Need Help to Find TV Series Main Topics of Grey’s AnatomyGrey’s Anatomy? Text mining, Visualization Series about ‘plane crash islandplane crash island’ Search engine What should I watch next? Recommender system amazon.com → 3 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac
- 4. Text Mining: Let’s Crunch Subtitles 4 Main Topics of Grey’s AnatomyGrey’s Anatomy? Text mining, Visualization Series about ‘plane crash islandplane crash island’ Search engine What should I watch next? Recommender system Cold CaseCold Case GreyGrey’s Anatomy’s Anatomy Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac
- 5. What’s in a Subtitle File? 5 Title – Season – Episode – Language.srt 1 episode = 1 plain text file Synchronization start --> stop Dialogue We can easily extract words [ a, again*2, and, but, com, cuban, different, favorite, food, for*2, forum, going, great, happen*2, has, hungry, i*2, is, it, love, m, my, nice, night*2, miami, now, pork, s*2, sandwiches, something, the, to*2, tonight, town, www ] Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac
- 6. 6 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac DB technology at Work! [Home] 7 527 files = 337 MB 100% Java and Oracle
- 7. DB technology at Work! [Search engine] 7 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Ranked list of results
- 8. DB technology at Work! [Infos] 8 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Most popular terms Most related series
- 9. DB technology at Work! [Recommendations] 9 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac
- 10. DB technology at Work! [Recommendations] 10 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac I liked I disliked What should I watch next?
- 11. DB technology at Work! [Recommendations] 11 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Ranked list of recommendations
- 12. How Does this Work? 12 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac
- 13. Architecture and Data Model 13 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac DB subtitles indexing searching browsing recommending GUI offline online Dict = { idT, term} 8 plane 27 killer 29 crash Posting = { idT*, idS*, nb} 27 45 89 8 45 3 8 12 90 ⊆ ⊆
- 14. Theory − Text Indexing Pipeline 14 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac [the, plane, crashed, ..., planes, ..., is] [plane, crashed, ..., planes, ...] [plane, crash, ..., plane, ...] {(plane, 48), (crash, 15) ...} Tokenization + lowercase Stopwords removal Stemming PorterPorter’s Stemmer (1980)’s Stemmer (1980) http://qaa.ath.cx/porter_js_demo.html In 1720 Robert Gordon retired to Aberdeen having amassed a considerable fortune in Poland. On his death 11 years later he willed his entire estate to build a residential school for educating young boys. In the summer of 1750 the Robert Gordon’s Hospital was born In 1881 this was converted into a day school to be known as Robert Gordon’s College. This school also began to hold day and evening classes for boys girls and adults in primary secondary mechanical and other subjects … Counting
- 15. Theory − Similarity of Paired Series 15 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac A Big Limitation The distribution of terms among series is ignored It makes no difference that a term occurs 1 time or 1,000,000 times Dice’s Coefficient (1945) Based on the Set Theory Example: Let us Model a Series as a Set of Terms House = {hospital, doctor, crazy, psycho} Grey’s = {doctor, care, hospital}
- 16. Vocabulary Theory − Vector Space Model, Term Weighting 16 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Raw TF dexter > lost max max Normalization TF / max(TF) survive ? max max dexter < lost
- 17. Theory − Best Match Retrieval 17 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac 1 TV series = 1 vector 1 45 1467 6790 n Now, we know how to: Find most popular termspopular terms for a TV series Compute similaritysimilarity between TV series Find TV series matching a querymatching a query
- 18. Theory − More on Term Weighting 18 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac 1 45 1467 6790 n 1 TV series = 1 vector All terms are supposed to be equally representative … but ‘survive’ is way more unusual than ‘people’ ⇒ ‘survive’ better represents Lost than ‘people’ does IDF: Inverse Document FrequencyIDF: Inverse Document Frequency
- 19. Theory − The Big Picture: TF*IDF 19 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac An important term for series S is frequent in Sis frequent in S and globally unusualglobally unusual.
- 20. Theory … and Practice 20 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Series = { idS, name, maxNb} 12 Lost 540 45 Dexter 125 Dict = { idT, term idf } 8 plane 1.25 27 killer 2.87 29 crash 3.07 Posting = { idT*, idS*, nb, tf } 27 45 89 0.71 8 45 3 0.02 8 12 90 0.16 ⊆ ⊆
- 21. Description of a TV Series 21 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac Lost ⋈ Many surnames need to be filtered out
- 22. Retrieval of TV Series − queries with 1 term 22 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac survive ⋈ Importance of normalization • Stargate Atlantis nb/maxNb = 63/1116 = 0.05645 • Blade nb/maxNb = 9/163 = 0.05521
- 23. Retrieval of TV Series − queries with n terms 23 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac survive mulder ⋈ 67|The Vampire Diaries survive|0.028|0.107 = 0.028 * 0.107 = 0.003 mulder|0.007|3.977 = 0.007 * 3.977 = 0.028 + 0.031 18| X-Files survive|0.014|0.107 = 0.014 * 0.107 = 0.001 mulder|1.000|3.977 = 1.000 * 3.977 = 3.977 + 3.978 ⁞
- 24. Similar to House? Computing Similarities Among TV Series 1/2 24 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac ⋈ First, let’s compute the numerator where: Ai = Terms from House Bi = Terms from Another TV series Ai Bi
- 25. Similar to House? Computing Similarities Among TV Series 2/2 Series-O-Rama: Search & Recommend TV series with SQL Guillaume Cabanac ⋈ ⋈ ⋈ 25
Editor's Notes
- select term, tf*idf score from posting p, dict d where p.idT = d.idT and idS = (select idS from series where name = 'Lost') order by 2 desc, 1 ;
- select name, term, nb, tf from posting p, series s, dict d where p.idS = s.idS and p.idT = d.idT and term = 'survive' order by tf desc, name ;
- select name, sum(tf*idf) rsv from posting p, series s, dict d where p.idS = s.idS and p.idT = d.idT and term in ('survive', 'mulder') group by p.idS, name order by 2 desc, 1 ;
- with numerator as ( select pLost.idS idLostS, pOther.idS idOtherS, sum(pLost.tf*idf * pOther.tf*idf) numValue from posting pLost, posting pOther, dict d where pLost.idT = pOther.idT -- common terms and pLost.idT = d.idT -- for IDF and pLost.idS <> pOther.idS and pLost.idS = (select idS from series where name = 'House') group by pLost.idS, pOther.idS ) select name, numValue / ( sqrt((select sum(power(tf*idf, 2)) from posting p, dict d where p.idT = d.idT and p.idS = n.idLostS)) * sqrt((select sum(power(tf*idf, 2)) from posting p, dict d where p.idT = d.idT and p.idS = n.idOtherS))) score from numerator n, series s where n.idOtherS = s.idS order by 2 desc, 1 ;
- with numerator as ( select pHouse.idS idHouseS, pOther.idS idOtherS, sum(pHouse.tf*idf * pOther.tf*idf) numValue from posting pHouse, posting pOther, dict d where pHouse.idT = pOther.idT -- common terms and pHouse.idT = d.idT -- for IDF and pHouse.idS <> pOther.idS and pHouse.idS = (select idS from series where name = 'House') group by pHouse.idS, pOther.idS ) select name, numValue / ( sqrt((select sum(power(tf*idf, 2)) from posting p, dict d where p.idT = d.idT and p.idS = n.idHouseS)) * sqrt((select sum(power(tf*idf, 2)) from posting p, dict d where p.idT = d.idT and p.idS = n.idOtherS))) score from numerator n, series s where n.idOtherS = s.idS order by 2 desc, 1 ;