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2009-06-28T08:57:29.287
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2009-06-28T08:57:29.287
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<p>I used the same reference (Bryan O'Sullivan's python code) to write an R version of Slope One a while back. I'm pasting the code below in case it helps.</p> <pre><code>predict <- function(userprefs, data.freqs, data.diffs) { seen <- names(userprefs) preds <- sweep(data.diffs[ , seen, drop=FALSE], 2, userprefs, '+') preds <- preds * data.freqs[ , seen] preds <- apply(preds, 1, sum) freqs <- apply(data.freqs[ , seen, drop=FALSE], 1, sum) unseen <- setdiff(names(preds), seen) result <- preds[unseen] / freqs[unseen] return(result[is.finite(result)]) } update <- function(userdata, freqs, diffs) { for (ratings in userdata) { items <- names(ratings) n <- length(ratings) ratdiff <- rep(ratings, n) - rep(ratings, rep(n, n)) diffs[items, items] <- diffs[items, items] + ratdiff freqs[items, items] <- freqs[items, items] + 1 } diffs <- diffs / freqs return(list(freqs=freqs, diffs=diffs)) } userdata <- list(alice=c(squid=1.0, cuttlefish=0.5, octopus=0.2), bob=c(squid=1.0, octopus=0.5, nautilus=0.2), carole=c(squid=0.2, octopus=1.0, cuttlefish=0.4, nautilus=0.4), dave=c(cuttlefish=0.9, octopus=0.4, nautilus=0.5)) items <- c('squid', 'cuttlefish', 'nautilus', 'octopus') n.items <- length(items) freqs <- diffs <- matrix(0, nrow=n.items, ncol=n.items, dimnames=list(items, items)) result <- update(userdata, freqs, diffs) print(result$freqs) print(result$diffs) userprefs <- c(squid=.4) predresult <- predict(userprefs, result$freqs, result$diffs) print(predresult) </code></pre>
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1. POWeighted slope one algorithm? (porting from Python to R)
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