Talk for Insight demos.

1. HackYrNews:
design a personalized feed
for Hacker News
Andrew Kontaxis

4. Let’s browse HN

5. Page 4…
Let’s browse HN

6. Page 4…
Page 7…
Let’s browse HN

7. Page 4…
Page 7…
Page 8…
Let’s browse HN

8. Page 4…
Page 7…
Page 8…
Page 10…
Let’s browse HN

9. Filtering posts given
user interests

10. Filtering posts given
user interests
Capture how interests are grouped within
the community.

11. Filtering posts given
user interests
Capture how interests are grouped within
the community.
Exploration, flexible input.

12. DEMO

13. Under the hood…
Data: post titles, comment text for 30K posts.

14. Under the hood…
Data: post titles, comment text for 30K posts.
Model: word2vec model trained on comment text.

15. Under the hood…
Data: post titles, comment text for 30K posts.
Model: word2vec model trained on comment text.
python
amazon
app
web
vocabulary
apple
Word2Vec space W
web
python
amazon
julia
app
apple
julia

16. Recommendation, Step 1
Recommending posts
1. Map each user interest to a vector.
2. Map each post to a vector.
3. For each interest, recommend the “nearest”
30 posts.

17. Recommendation, Step 1
Recommending posts
1. Map each user interest to a vector.
2. Map each post to a vector.
3. For each interest, recommend the “nearest”
30 posts.

18. Word2Vec space W
Interests
apple,
+python|-web

19. Word2Vec space W
Interests
apple,
+python|-web
apple

20. Word2Vec space W
python
webInterests
apple,
+python|-web
apple
-web

21. Word2Vec space W
python
webInterests
apple,
+python|-web
apple
-web+python|-web

22. Recommendation, Step 1
Recommending posts
1. Map each user interest to a vector.
2. Map each post to a vector.
3. For each interest, recommend the “nearest”
30 posts.

23. Word2Vec space W
Post #7:
Amazon App store

24. Word2Vec space W
amazon
app
store
Post #7:
Amazon App store

25. Word2Vec space W
Post #7:
Amazon App store
amazon
app
store
Post #7

26. Recommendation, Step 1
Recommending posts
1. Map each user interest to a vector.
2. Map each post to a vector.
3. For each interest, recommend the “nearest”
30 posts.

27. Word2Vec space W
Interests
apple,
+python|-web apple
+python|-web
Post #7

28. Word2Vec space W
Interests
apple,
+python|-web apple
+python|-web
Post #7
Suggestions
Post #7:
“Amazon app store”

29. Recommendation, Step 1
Recommending posts
1. Map each user interest to a vector.
2. Map each post to a vector.
3. For each interest, recommend the “nearest”
30 posts.

30. About me
Interests:
+cooking|-baking,
+banjo|-bluegrass

35. Why this setup?
• Initial thought: some kind of topic modeling.
• User input is inflexible (topics are incoherent if we
use more than 10).
• Ideal use case is streaming, but number of topics
must be chosen manually, and “meanings” must
be assigned to topics manually.
• Similar remarks apply to clustering + dimension
reduction.