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RapStar’s Solution to Data Mining Hackathon on Best Buy Mobile Site

RapStar’s Solution to Data Mining Hackathon on Best Buy Mobile Site . Kingsfield , Dragon. Beat Benchmark. Beat Benchmark. Naive Bayes We want to know the probability that user click sku under context . We use query as context first. So we have:

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RapStar’s Solution to Data Mining Hackathon on Best Buy Mobile Site

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  1. RapStar’s Solution to Data Mining Hackathon on Best Buy Mobile Site Kingsfield, Dragon

  2. Beat Benchmark

  3. Beat Benchmark • Naive Bayes • We want to know the probability that user click sku under context . • We use queryas context first. • So we have: • Select 5 item with highest predicted probability as prediction.

  4. Use Time information • Time is a good feature in data mining.

  5. Use Time information • Divided data into 12 time periods based on click_time field • Use frequency at time period where click_time belongs to as “prior” instead of global frequency.

  6. Use Time information • Smooth data

  7. Unigram to Bigram • Likelihood of Naive Bayes: • Here is word. • Use Bigram instead of Unigram(word). • Use query “xbox call of duty” • Rerank: “call duty of xbox” • Bigram: [“call duty”, ”call of”, ”call xbox”… “of xbox”] • Once We have bigram training data, the rest is the same as unigram • Blending unigram and bigram:

  8. Data Processing • The most important part: Query Correction • Lemmatization • Split words and number • Query correction(in small version) • A lot of thing that can help to improve: • “x box”, “x men” • New algorithm for query correction • Rank predictions that user clicked lower.

  9. Conclusion • Data Preprocessing and feature Engineering are most important things.

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