Comments:

Nice article : easy to read, recommendation method is quite straightforward and efficient considering the task, impressive dataset. ## Summary : ### Introduction * two aspects underexplored in the field of news consumption : increase of shortcuts to news portals, browsing behavior in news consumption * focus on the influence of the referrer on the user behavior (to predict where they go) * data 500 millions viewlogs of Yahoo News * compare 24 types of recommendation (actually 24 flavors as the principle is quite similar from a method to another) * contributions: browse graph definition, study the browse graph on their dataset, provide recommendation method for next article to read ### Related work * problem of cold start recommendation, one possible way to circumvent is to use a small set of preferences ("warm start") => how to use the little amount of information available (like the social network) * here, info = referrer URL and current reading * literature for news recommendation : use predominantly user history ; here sort of collaborative filtering * browse graph and referrer URL : ref analyzing browsing sessions ; browse graph already used in literature ### Browse graph in the news domain ##### dataset : - Yahoo pages have infinite recommendation when scrolling - cookie contains : current URL, referrer URL, temporal information, brwoser information - split in sessions with timeout 25 minutes - 22 article topics (editorially assigned) - see tab3 for examples ##### about the browsegraph : - definition : aggregated graph of transitions, with weights - paper focus : contents differences depending of the origin - process : breaking down the browsegraph depending of the referrer (Twitter, Facebook, search engine, etc) - hourly separation ### Analysis ##### description referrer graphs : - browsing sessions are short but typical distance in GWCC of the browse graph is 5 (cf tab2) - all referrer graphs have a GWCC which contains more than around 90% of nodes - degree distributions vary a lot from one to another, weight distributions don't (fig2) - testing if RG vary from one to the other in terms of nodes => measure overlap with Jaccard index and Kendall-tau between pageranks (fig3) => two major groups : search vs social - most popular topics per RG : see tab3 ##### evolution through time : - fig4 : cumulative number of views => 80% visits during first 30hrs and first 20% of lifespan (consistent with literature) - does it vary with RG considered ? - fig5 : 3 categories homepage / search / social => rapid decay in three cases, social exhibits consumption spikes later in their life span - fig6 : topic influence => most cases homepage > search > social ; standard dynamic (sports, movies, blogs) : search starts close to social and then gets closer to homepage - fig7 : change through time, Kendall tau (rank at time t=0 vs rank at time t) => decrease then steady after roughly 24h (questionable observation from my pov), lesser offset for search than for the rest ### Cold-start prediction of next view ##### problem definition : - predicting page seen after starting page - restriction to users who have at least one page view after starting page ##### selection of candidate pages : - full : all (out)neighbors in the Browse graph - ref : or all neighbors in the Referrer graph only - or mixed : if no proposition in the RG then full BG ##### topical filtering : - case of Twitter and Facebook : possible additional constraint to search into the same category ##### prediction method : - random (baseline) - cb : most similar in content, using text-based metrics - pop: highest view count at previous timestep - edge: maximum weight link from the same node at the previous timestep ##### results (Fig8) : - quality measures : precision and mean reciprocal ranking@3 - trend : random < pop ~< cb < edge => (overall conclusion) weights of the BG effective to anticipate user needs - trend : full < ref < mixed - precision increases with smaller domains, just because the set of possibilities is smaller - trend : using topical filtering => drop in precision (because high probability of topic transition)
Read the paper, add your comments…

Comments:

[Rob Pike](http://www.herpolhode.com/rob/) complains about software systems research. A decade later he designed the [go](https://en.wikipedia.org/wiki/Go_(game) language together with Robert Griesemer and Ken Thompson.
Read the paper, add your comments…

Comments:

Well, apparently nobody knows how to enumerate directed animals according to the number of edges. It is an open question of combinatorics. The following table from "Directed Animals on Two Dimensional Lattices" article by A. R. Conway, R. Brak and A. J. Guttmann presents results for n<40 **Number of bond animals on the square lattice...** ``` 1 1 2 2 3 5 4 14 5 42 6 130 7 412 8 1326 9 4318 10 14188 11 46950 12 156258 13 522523 14 1754254 15 5909419 16 19964450 17 67618388 18 229526054 19 780633253 20 2659600616 21 9075301990 22 31010850632 23 106100239080 24 363428599306 25 1246172974048 26 4277163883744 27 14693260749888 28 50516757992258 29 173812617499767 30 598455761148888 31 2061895016795926 32 7108299669877836 33 24519543126693604 34 84623480620967174 35 292204621065844292 36 1009457489428859322 37 3488847073597306764 38 12063072821044567580 39 41725940730851479532 40 144383424404966638976 ```
Read the paper, add your comments…

Comments:

How software verification paradigm deals with Thompson hack?
Read the paper, add your comments…

Comments:

Read the paper, add your comments…
Pages: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28