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Get Recced: App Recommendation using Reviews and Ratings

Abstract:

In this project we examined how recommender systems work (better or worse) if we take advantage of the review texts along with the review ratings. We aimed to combine latent ratings with latent review topics and analyze the results. Our assumption was that combining the review text with ratings would help the recommender system make better predictions. Hence, we compared different models on Amazon Apps dataset and calculated RMSE.

We Compared and evaluated the following recommendation models:

• Baseline model
• Collaborative Filtering
• Latent Dirichlet Allocation (LDA)
• Hidden Factors as Topics (HFT)

Datasets Used:

Amazon App Dataset:

{
“reviewerID”: “A1N4O8VOJZTDVB”,
“asin”: “B004A9SDD8”,
“reviewerName”: “Annette Yancey”,
“helpful”: [1, 1],
“reviewText”: “Loves the song, so he really couldn’t wait to play this. A little less interesting for him so he doesn’t play long, but he is almost 3 and likes to play the older games, but really cute for a younger child.”, “overall”: 3.0,
“summary”: “Really cute”,
“unixReviewTime”: 1383350400,
“reviewTime”: “11 2, 2013”
}

{
“reviewerID”: “A2HQWU6HUKIEC7”,
“asin”: “B004A9SDD8”,
“reviewerName”: “Audiobook lover \”Kathy\"",
“helpful”: [0, 0],
“reviewText”: “Oh, how my little grandson loves this app. He’s always asking for \”Monkey.\" Grandma has tired of it long before he has. Finding the items on each page that he can touch and activate is endlessly entertaining for him, at least for now. Well worth the $.99.", “overall”: 5.0, “summary”: “2-year-old loves it”,
“unixReviewTime”: 1323043200,
“reviewTime”: “12 5, 2011”
}

Data Distribution:

Half of the ratings are 5s

Average rating is 3.96